增加交易策略、交易指标、量化库代码等文件夹
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1.交易策略/999.其他策略/1.松鼠SF08_基于盘口数据的择时趋势策略/使用文档/过程文件/real_trade.py
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780
1.交易策略/999.其他策略/1.松鼠SF08_基于盘口数据的择时趋势策略/使用文档/过程文件/real_trade.py
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'''
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使用说明：使用前需要调整的相关参数如下
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1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
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2.配置邮件信息和参数。
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3.tickdata函数中：一、修改时间冲采样resample中rule周期5T为交易周期，
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4.GetOrderFlow_dj函数：一、堆积函数config参数暂时均为3
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5.MyTrader类:
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1) init函数初始化:委托价格的偏移、跟踪出场、固定出差参数、交易手数的设置；
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2) day_data_reset函数、每日收盘重置数据按照交易品种设置。
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3）Join函数：修改“开多组合”和“开空组合”
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6. __main__函数：设置交易账户变量
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该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
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on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
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tickdata(df, symbol): 处理Tick数据，生成K线数据。
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orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
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GetOrderFlow_dj(kData): 计算订单流的信号指标。
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除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
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'''
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# 需要完善：__main__函数中手动设置subscribe_list变量，通过时间判断是否需要进行换月修改，并发送邮件通知
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from multiprocessing import Process, Queue
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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
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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 = ["***@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 = "***@qq.com" # 设置发送邮件的邮箱地址
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username = "***@qq.com" # 设置发送邮件的邮箱用户名
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password = "zrmpcgttataabhjh" #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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trader_df = 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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# 邮件通知模块
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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 tickcome(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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# 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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on_tick(tick)
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def can_time(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(tick):
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tm=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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tickdata(tick_dt,sym)
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# 这个函数的主要目的是将输入的买盘和卖盘字典合并、排序、累加，并将处理后的结果存储在一个全局字典quotedict中，同时返回这个结果。
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def data_of(df):
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global trader_df
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# 将df数据合并到trader_df中
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trader_df = pd.concat([trader_df, df], ignore_index=True)
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#print('trader_df: ', len(trader_df))
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#print(trader_df)
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def process(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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def tickdata(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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data_of(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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bardata = tickdata.resample(on = 'bartime',rule = '1T',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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orderflow_df_new(tickdata,bardata,symbol)
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# time.sleep(0.5)
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def orderflow_df_new(df_tick,df_min,symbol):
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startArray = pd.to_datetime(df_min['starttime']).values
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voluememin= df_min['volume'].values
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highs=df_min['high'].values
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lows=df_min['low'].values
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opens=df_min['open'].values
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closes=df_min['close'].values
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#endArray = pd.to_datetime(df_min['bartime']).values
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endArray = df_min['bartime'].values
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#print(endArray)
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deltaArray = np.zeros((len(endArray),))
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tTickArray = pd.to_datetime(df_tick['datetime']).values
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bp1TickArray = df_tick['bid_p'].values
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ap1TickArray = df_tick['ask_p'].values
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lastTickArray = df_tick['lastprice'].values
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volumeTickArray = df_tick['volume'].values
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symbolarray = df_tick['symbol'].values
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indexFinal = 0
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for index,tEnd in enumerate(endArray):
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dt=endArray[index]
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start = startArray[index]
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bidDict = {}
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askDict = {}
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bar_vol=voluememin[index]
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bar_close=closes[index]
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bar_open=opens[index]
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bar_low=lows[index]
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bar_high=highs[index]
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bar_symbol=symbolarray[index]
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# for indexTick in range(indexFinal,len(df_tick)):
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# if tTickArray[indexTick] >= tEnd:
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# break
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# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
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Bp = round(bp1TickArray[0],4)
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Ap = round(ap1TickArray[0],4)
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LastPrice = round(lastTickArray[0],4)
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Volume = volumeTickArray[0]
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if LastPrice >= Ap:
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if str(LastPrice) in askDict.keys():
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askDict[str(LastPrice)] += Volume
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else:
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askDict[str(LastPrice)] = Volume
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if LastPrice <= Bp:
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if str(LastPrice) in bidDict.keys():
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bidDict[str(LastPrice)] += Volume
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else:
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bidDict[str(LastPrice)] = Volume
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# indexFinal = indexTick
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bidDictResult,askDictResult = process(bidDict,askDict,symbol)
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bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
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askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
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prinslist=list(bidDictResult.keys())
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asklist=list(askDictResult.values())
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bidlist=list(bidDictResult.values())
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delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
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#print(prinslist,asklist,bidlist)
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#print(len(prinslist),len(bidDictResult),len(askDictResult))
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df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
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#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
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df['symbol']=bar_symbol
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df['datetime']=dt
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df['delta']=str(delta)
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df['close']=bar_close
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df['open']=bar_open
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df['high']=bar_high
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df['low']=bar_low
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df['volume']=bar_vol
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#df['ticktime']=tTickArray[0]
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df['dj'] = GetOrderFlow_dj(df)
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ofdatadict[symbol]=df
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#公众号：松鼠Quant
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#主页：www.quant789.com
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#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def GetOrderFlow_dj(kData):
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Config = {
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'Value1': 3,
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'Value2': 3,
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'Value3': 3,
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'Value4': True,
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}
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aryData = kData
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djcout = 0
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# 遍历kData中的每一行，计算djcout指标
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for index, row in aryData.iterrows():
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kItem = aryData.iloc[index]
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high = kItem['high']
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low = kItem['low']
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close = kItem['close']
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open = kItem['open']
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dtime = kItem['datetime']
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price_s = kItem['price']
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Ask_s = kItem['Ask']
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Bid_s = kItem['Bid']
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delta = kItem['delta']
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price_s = price_s
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Ask_s = Ask_s
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Bid_s = Bid_s
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gj = 0
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xq = 0
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gxx = 0
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xxx = 0
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# 遍历price_s中的每一个元素，计算相关指标
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||||
for i in np.arange(0, len(price_s), 1):
|
||||
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
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
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):
|
||||
self.py=5 #设置委托价格的偏移，更加容易促成成交。仅螺纹，其他品种根据最小点波动，自己设置
|
||||
self.cont_df=0
|
||||
self.trailing_stop_percent = 0.02 #跟踪出场参数
|
||||
self.fixed_stop_loss_percent = 0.01 #固定出场参数
|
||||
self.dj_X=1 #开仓的堆积参数
|
||||
|
||||
self.pos=0
|
||||
self.Lots=1
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.sl_shor_price=0
|
||||
self.out_long=0
|
||||
self.out_short=0
|
||||
self.clearing_executed=False
|
||||
self.kgdata=True
|
||||
|
||||
|
||||
#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
|
||||
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 self.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
|
||||
self.pos = int(row['pos'])
|
||||
self.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
self.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
self.sl_long_price = float(row['sl_long_price'])
|
||||
self.sl_shor_price = float(row['sl_shor_price'])
|
||||
# self.out_long = int(row['out_long'])
|
||||
# self.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])
|
||||
self.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):
|
||||
# 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': [trader_df['datetime'].iloc[-1]],
|
||||
'pos': [self.pos],
|
||||
'short_trailing_stop_price': [self.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [self.long_trailing_stop_price],
|
||||
'sl_long_price': [self.sl_long_price],
|
||||
'sl_shor_price': [self.sl_shor_price],
|
||||
# 'out_long': [self.out_long],
|
||||
# 'out_short': [self.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):
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
|
||||
# 第二时间范围
|
||||
clearing_time2_start = s_time(23,0)
|
||||
clearing_time2_end = s_time(23,15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
self.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 self.clearing_executed :
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.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
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not self.clearing_executed :
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.index,inplace=True) #清除当天的行情数据
|
||||
else:
|
||||
self.clearing_executed = False
|
||||
pass
|
||||
return self.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 Join(self):
|
||||
data = None
|
||||
while True:
|
||||
if self.status == 0:
|
||||
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset()
|
||||
tickcome(data)
|
||||
#新K线开始，启动交易程序 and 保存行情数据
|
||||
if len(trader_df)>self.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
|
||||
trader_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
|
||||
trader_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if self.long_trailing_stop_price >0 and self.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = trader_df['low'].iloc[-1] if self.long_trailing_stop_price<trader_df['low'].iloc[-1] else self.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price >0 and self.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = trader_df['high'].iloc[-1] if trader_df['high'].iloc[-1] <self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long >0:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'预设——多头止盈——','TR',self.out_long,'low', trader_df['low'].iloc[-1])
|
||||
if trader_df['low'].iloc[-1] < self.out_long and self.pos>0 and self.sl_long_price>0 and trader_df['low'].iloc[-1]>self.sl_long_price:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'多头止盈','TR',self.out_long,'low', trader_df['low'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.long_trailing_stop_price = 0
|
||||
self.out_long=0
|
||||
self.sl_long_price=0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if self.out_short>0:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',self.out_short,'high', trader_df['high'].iloc[-1])
|
||||
if trader_df['high'].iloc[-1] > self.out_short and self.pos<0 and self.sl_shor_price>0 and trader_df['high'].iloc[-1]<self.sl_shor_price:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'空头止盈: ','TR',self.out_short,'high', trader_df['high'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.pos>0:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '预设——多头止损', 'SL', self.fixed_stop_loss_L, 'close', trader_df['close'].iloc[-1])
|
||||
if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and trader_df['close'].iloc[-1] < self.fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '多头止损', 'SL', self.fixed_stop_loss_L, 'close', trader_df['close'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.pos<0:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '预设——空头止损', 'SL', self.fixed_stop_loss_S, 'close', trader_df['close'].iloc[-1])
|
||||
if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and trader_df['close'].iloc[-1] > self.fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '空头止损', 'SL', self.fixed_stop_loss_S, 'close', trader_df['close'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#日均线
|
||||
trader_df['dayma']=trader_df['close'].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trader_df['delta'] = trader_df['delta'].astype(float)
|
||||
trader_df['delta累计'] = trader_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trader_df['dayma'].iloc[-1] > 0 and trader_df['close'].iloc[-1] > trader_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trader_df['delta累计'].iloc[-1] > 2000 and trader_df['delta'].iloc[-1] > 1500
|
||||
|
||||
#小于日均线
|
||||
开空1=trader_df['dayma'].iloc[-1]>0 and trader_df['close'].iloc[-1] < trader_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trader_df['delta累计'].iloc[-1] < -2000 and trader_df['delta'].iloc[-1] < -1500
|
||||
|
||||
开多组合= 开多1 and 开多4 and trader_df['dj'].iloc[-1]>self.dj_X
|
||||
开空条件= 开空1 and 开空4 and trader_df['dj'].iloc[-1]<-self.dj_X
|
||||
|
||||
平多条件=trader_df['dj'].iloc[-1]<-self.dj_X
|
||||
平空条件=trader_df['dj'].iloc[-1]>self.dj_X
|
||||
#开仓
|
||||
#多头开仓条件
|
||||
if self.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+self.py)
|
||||
#insert_order:买卖方向，开仓：0，平仓：1，强平：2，平今：3，平昨：4，强减：5，本地强平：6
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.pos=0
|
||||
self.sl_shor_price=0
|
||||
self.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+self.py,'堆积数：', trader_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{trader_df['datetime'].iloc[-1]}, 反手平空的平仓价格{data['AskPrice1']+self.py}"
|
||||
send_mail(text)
|
||||
if self.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+self.py)
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.pos=1
|
||||
self.long_trailing_stop_price=data['AskPrice1']
|
||||
self.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{trader_df['datetime'].iloc[-1]}, 多头开仓的开仓价格{data['AskPrice1']+self.py}，预设——多头止盈——TR{self.out_long},多头止损SL{self.fixed_stop_loss_L}"
|
||||
send_mail(text)
|
||||
|
||||
|
||||
if self.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-self.py)
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.pos=0
|
||||
self.long_trailing_stop_price=0
|
||||
self.sl_long_price=0
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-self.py,'堆积数：', trader_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{trader_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-self.py}"
|
||||
send_mail(text)
|
||||
|
||||
if self.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']-self.py,self.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.pos=-1
|
||||
self.short_trailing_stop_price=data['BidPrice1']
|
||||
self.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{trader_df['datetime'].iloc[-1]}, 空头开仓的开仓价格{data['BidPrice1']-self.py},预设——空头止盈——TR{self.out_short},空头止损{self.fixed_stop_loss_S}"
|
||||
send_mail(text)
|
||||
|
||||
print(trader_df)
|
||||
self.cont_df=len(trader_df)
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
|
||||
def run_trader(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.Join()
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
future_account = get_simulate_account(
|
||||
investor_id='135858', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
password='Zj82334475', # simnow密码
|
||||
server_name='TEST', # 电信1、电信2、移动、TEST、N视界
|
||||
subscribe_list=[b'rb2405'], # 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': "ip:port", 'MDServer': 'ip:port'}, # 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='simnow_client_test', # 认证使用AppID
|
||||
# auth_code='0000000000000000', # 认证使用授权码
|
||||
# subscribe_list=[b'rb2405'], # 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=(
|
||||
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()
|
||||
# success = f"行情和交易启动成功！{future_account.subscribe_list}"
|
||||
# send_mail(success)
|
||||
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user