#这里给大家 展示一下怎么运用咱们supermind的 tick级别数据制作集合竞价 高频因子 的方法
#一般竞价多头总数>0的股票 证明 集合竞价主力有在运作，在该日博弈有较大概率 可以买到涨停的股票
import pandas as pd
import numpy as np
import datetime
from mindgo_api import get_tick

import matplotlib.pyplot as plt  
ks=['603662.SH']
s1='2023-05-29'
cf=get_tick(ks,"{0} 09:20".format(s1),"{0} 09:25:00".format(s1),['total_bid','total_ask','volume','b1_v','b2_v','b3_v','a1_v','a2_v','a3_v','cjcs'])
cf = cf.fillna(0)
cf['多空线_1']=cf['total_bid']-cf['total_ask']

cf['竞价线']=cf['b1_v']+cf['b2_v']-cf['a1_v']-cf['a2_v']
    # 设置画布大小
plt.figure(figsize=(10, 5))
cf['trade_date'] = pd.to_datetime(cf['trade_date'])  # 将日期字符串转换为时间序列类型

cf['竞价成交量因子']=cf['竞价线'].values[-1]-cf['竞价线'].values[0]
cf['竞价多头总数因子']=(cf['竞价线'].values >0).sum()


df_new = cf.drop_duplicates(subset=['id_stock', '竞价成交量因子','竞价多头总数因子'], keep='last')
data = df_new[['id_stock','竞价成交量因子','竞价多头总数因子']]
data.columns = ['股票代码','竞价成交量因子','竞价多头总数因子']    

plt.plot(cf['trade_date'],cf['竞价线'])
#plt.plot(cf['trade_date'],cf['多空线_1'])
print(data)
#策略v详x细 agjyy20191026 

cf