stock/QMT/1m_real_time.py

# coding:utf-8
from datetime import datetime as dt
import os
import pandas as pd
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount
from xtquant import xtdata, xtconstant
import time
from sqlalchemy import create_engine, text
from jqdatasdk import *
import pymysql
import multiprocessing as mp
import math
import psutil
import datetime
from apscheduler.schedulers.blocking import BlockingScheduler
import sys


df = pd.DataFrame(columns=['time', 'open', 'close', 'high', 'low', 'volume', 'amount'])

def avg_price(df):
        print('avg_price')
        daily_avg_price = []
        for i in range(1, len(df) + 1):
            daily_avg_price.append(round(sum(df['amount'][:i]) / sum(df['volume'][:i]) / 100, 2))
        df['daily_avg_price'] = daily_avg_price
        return df

def over_avg(datas):
    global df
    for stock_code in datas:
        df_temp = pd.DataFrame(datas[stock_code])
        df_temp['time'] = df_temp['time'].apply(lambda x: dt.fromtimestamp(x / 1000.0))
        df = pd.concat([df, df_temp], axis=0, ignore_index=True)
        # df = avg_price(df)
        # df = df[['time', 'open', 'close', 'high', 'low', 'volume', 'amount', 'daily_avg_price']]
        print(df)
        # print(stock_code, datas[stock_code])


if __name__ == '__main__':
    print('start')
    t = dt.now().strftime('%Y%m%d')
    # stocks = xtdata.get_stock_list_in_sector('沪深A股')
    xtdata.subscribe_quote('000001.SH', period='1m', start_time=t, callback=over_avg)
    xtdata.run()