JUKUAN/Lib/stock/strategy_1.py

# 克隆自聚宽文章：https://www.joinquant.com/post/53374
# 标题：机位有限，实盘策略送人
# 作者：韶华不负

# 导入函数库
from jqdata import *
from six import BytesIO
from jqlib.technical_analysis  import *
from jqfactor import get_factor_values
import numpy as np
import pandas as pd 
import time

# 初始化函数，设定基准等等
def after_code_changed(context):
    # 输出内容到日志 log.info()
    log.info('初始函数开始运行且全局只运行一次')
    unschedule_all()
    # 过滤掉order系列API产生的比error级别低的log
    # log.set_level('order', 'error')
    set_params()    #1 设置策略参数
    set_variables() #2 设置中间变量
    set_backtest()  #3 设置回测条件
    disable_cache()

    ### 股票相关设定 ###
    # 股票类每笔交易时的手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')

    ## 运行函数（reference_security为运行时间的参考标的；传入的标的只做种类区分，因此传入'000300.XSHG'或'510300.XSHG'是一样的）
      # 开盘前运行
    run_daily(before_market_open, time='6:00')
      # 竞价时运行
    run_daily(call_auction, time='09:25:15')
      # 开盘时运行-测试不同的买入时间
    #run_daily(market_open, time='09:45')
      # 收盘时运行
    run_daily(market_run, time='14:55')
      # 收盘后运行
    #run_daily(after_market_close, time='20:00')
          # 收盘后运行
    #run_daily(after_market_analysis, time='21:00')

#1 设置策略参数
def set_params():
    #设置全局参数
    g.index ='all'          #all-zz-300-500-1000，single-个股信号诊断
    g.code = '000679.XSHE'
    
    g.switch = 10            #1-低位首板低开，2-首阳倒挂，10-两者合并
    
    #池1-低板低开
    #买前量价过滤
    g.check_dura_1 =5        #默认是20，可以测试10-60
    g.pre_rise_1 = 1.1        #前期(20天)的涨幅上限,10等同不设限
    
    g.optimz_control = 1     #0-默认不控制，1-采用板型过滤
    g.volume_control_1 = 2    #0-默认不控制，1-周期放量控制,2-周期倍量控制,3,倍量控制(相对昨日),4-放量(240-0.9)加倍量(20-5)的最佳回测叠加
    g.volume_period_1 = 20   #放量控制周期，240-120-90-60
    g.volume_ratio_1 = 5    #放量控制和周期最高量的比值，0.9/0.8
    #竞价面
    g.auction_open_highlimit_1 = 0.98  #竞价开盘上限,999-不设限
    g.auction_open_lowlimit_1 = 0 #竞价开盘下限，0-不设限，1-相当于高开
    
    g.begin_times = ' 09:24:55'
    g.end_times =  ' 09:25:10'
    
    #池2-低板倒挂
    g.check_dura_2 =20        #默认是20，可以测试10-60
    g.pre_rise_2 = 1.1        #前期(20天)的涨幅上限,10等同不设限
    g.strong_limit = 1.06   #D日的涨幅下限
    g.trap_limit = 1.06     #D1日的高开套下限
    
    g.filt_type = 'S'       #进行二次择优，B-基本面，V-量价面,S-静态(流通盘)
    g.cirm_up = 45         #流通盘过滤上线,9999-不设限
    
    g.volume_control_2 = 2    #0-默认不控制，1-周期放量控制,2-周期倍量控制,3,倍量控制(相对昨日),4-放量(240-0.9)加倍量(20-5)的最佳回测叠加
    g.volume_period_2 = 20   #放量控制周期，240-120-90-60
    g.volume_ratio_2 = 5    #放量控制和周期最高量的比值，0.9/0.8
    
    #竞价面
    g.auction_open_highlimit_2 = 0.98  #竞价开盘上限,999-不设限;设定低开更有效
    g.auction_turn_highlimit_2 = 0 #竞价开盘下限，0-不设限，1-相当于高开

#2 设置中间变量
def set_variables():
    #暂时未用，测试用全池
    g.stocknum = 0              #持仓数，0-代表全取，2/4/8，因为地板倒和低板低各取1
    
#3 设置回测条件
def set_backtest():
    ## 设定g.index作为基准
    if g.index == 'all':
        set_benchmark('000001.XSHG')
    elif g.index == 'single':
        set_benchmark(g.code)
    else:
        set_benchmark(g.index)
    # 开启动态复权模式(真实价格)
    set_option('use_real_price', True)
    set_option("avoid_future_data", True)
    #显示所有列
    pd.set_option('display.max_columns', None)
    #显示所有行
    pd.set_option('display.max_rows', None)
    log.set_level('order', 'error')    # 设置报错等级
    
## 开盘前运行函数
def before_market_open(context):
    log.info('----------------------------------------------------------------')
    log.info('------------------------美好的一天开始了------------------------')
    log.info('----------------------------------------------------------------')
    # 输出运行时间
    log.info('函数运行时间(before_market_open)：'+str(context.current_dt.time()))
    #0，预置全局参数
    today_date = context.current_dt.date()
    lastd_date = context.previous_date
    befor_date = get_trade_days(end_date=today_date, count=3)[0]
    all_data = get_current_data()
    g.pool_1 = []
    g.pool_2 = []

    num1,num2,num3,num4,num5,num6=0,0,0,0,0,0    #用于过程追踪
            
    #0，构建基准指数票池，三去+去新
    start_time = time.time()
    if g.index =='all':
        stocklist = list(get_all_securities(['stock']).index)   #取all
    elif g.index == 'zz':
        stocklist = get_index_stocks('000300.XSHG', date = None) + get_index_stocks('000905.XSHG', date = None) + get_index_stocks('000852.XSHG', date = None)
    elif g.index =='single':
        stocklist = [g.code]    #个股信号诊断
    else:
        stocklist = get_index_stocks(g.index, date = None)
    
    num1 = len(stocklist)    
    stocklist = [stockcode for stockcode in stocklist if not all_data[stockcode].paused]
    stocklist = [stockcode for stockcode in stocklist if not all_data[stockcode].is_st]
    stocklist = [stockcode for stockcode in stocklist if'退' not in all_data[stockcode].name]
    stocklist = [stockcode for stockcode in stocklist if stockcode[0:3] != '688']
    stocklist = [stockcode for stockcode in stocklist if (today_date-get_security_info(stockcode).start_date).days>20]
    #stocklist = [stockcode for stockcode in stocklist if stockcode[0:1] == '3'] #专为3字头
    num2 = len(stocklist)
    
    end_time = time.time()
    print('Step0,基准%s,原始%d只,四去后共%d只,构建耗时:%.1f 秒' % (g.index,num1,num2,end_time-start_time))
    
    
    if g.switch ==1 or g.switch ==10:
        #1.1,池1过滤
        start_time = time.time()
        list_201 = get_up_filter_jiang(context,stocklist,lastd_date,1,1,0)
        list_201 = get_up_filter_jiang(context,list_201,lastd_date,20,1,0)
        num3 = len(list_201)
        
        end_time = time.time()
        print('Step1.1,%d日首板共%d只，相对位置过滤共%d只,构建耗时:%.1f 秒' % (20,num3,len(list_201),end_time-start_time))
        log.info(list_201)

        #2.1,池1优化
        start_time = time.time()
        list_201 = get_rise_filter(context,list_201,lastd_date,g.check_dura_1,g.pre_rise_1,-1)
        
        if g.optimz_control !=0:
            g.pool_1 = get_up_optimize_filter(context,list_201)
            num4 = len(g.pool_1)
        else:
            g.pool_1 = list_201
        
        if g.volume_control_1 !=0:
            g.pool_1 = get_highvolume_filter(context,g.pool_1,g.volume_control_1,g.volume_period_1,g.volume_ratio_1)
        else:
            g.pool_1 = list_201    

        end_time = time.time()
        print('Step2.1,涨幅限制%.2f共%d只,板型优化共%d只,量能控制共%d只,构建耗时:%.1f 秒' % (g.pre_rise_1,len(list_201),num4,len(g.pool_1),end_time-start_time))
        log.info('地板待选:%s' % g.pool_1)
    
    if g.switch ==2 or g.switch ==10:
        #1.2，低位票(ROC20<1.1)，D日穿线大阳(7点以上)或涨停后，D1日高开套人(开0点上下落7点以上)+不能反包D日，
        start_time = time.time()
        list_66 = get_rise_filter(context,stocklist,lastd_date,g.check_dura_2,g.pre_rise_2,-1)
        num3 = len(list_66)
        list_66 = get_rainbow_strong_filter(context,list_66,befor_date,g.strong_limit)
        num4 = len(list_66)
        list_66 = get_trap_filter(context,list_66,lastd_date,g.trap_limit)
    
        end_time = time.time()
        print('Step1.21,周期涨幅低于%.2f共%d只,前日%.2f强势共%d只,昨日发套%.2f共%d只,构建耗时:%.1f 秒' % (g.pre_rise_2,num3,g.strong_limit,num4,g.trap_limit,len(list_66),end_time-start_time))
        log.info(list_66)
        
        if g.volume_control_2 !=0:
            list_66 = get_highvolume_filter(context,list_66,g.volume_control_2,g.volume_period_2,g.volume_ratio_2)
            end_time = time.time()
            print('Step1.22,量能控制共%d只,构建耗时:%.1f 秒' % (len(list_66),end_time-start_time))
            log.info(list_66)
        #2.2，从基本面，量价面进行择优
        start_time = time.time()
        if g.filt_type !=False:
            g.pool_2 = get_trap_optimize_filter(context,list_66,lastd_date,g.filt_type)
    
        end_time = time.time()
        print('Step2.2,%s择优共%d只,构建耗时:%.1f 秒' % (g.filt_type,len(g.pool_2),end_time-start_time))
        log.info('半岛待选:%s' % g.pool_2)

    
def call_auction(context):
    log.info('函数运行时间(Call_auction)：'+str(context.current_dt.time()))
    
    open_list=[]
    current_data = get_current_data()
    today_date = context.current_dt.date()
    lastd_date = context.previous_date
    g.buy_list=[]
    
    if len(g.pool_2) !=0:
        df_trap= pd.DataFrame(columns=['code','price','open','turn'])
        
        df_auction = get_call_auction(g.pool_2,start_date=today_date,end_date=today_date,fields=['time','current','volume','money'])
        for i in range(len(df_auction)):
            stockcode = df_auction.code.values[i]
            price = df_auction.current.values[i]
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['close','high'],count=5)
            open = price/df_price.close[-1]
            turn = price/df_price.high[-1]
            df_trap = df_trap.append({'code': stockcode,'price': price,'open': open,'turn': turn}, ignore_index=True)
            
        df_trap = df_trap[(df_trap.open < g.auction_open_highlimit_2)] #竞开过滤

        df_trap.sort_values(by='turn', ascending=True, inplace=True)
        turn_list = list(df_trap.code)[:round(0.5*len(g.pool_2))] #发版
 

        g.buy_list = turn_list
        
    if len(g.pool_1) !=0:
        df_auction = get_call_auction(g.pool_1,start_date=today_date,end_date=today_date,fields=['time','current','volume','money'])
        
        for i in range(len(df_auction)):
            stockcode = df_auction.code.values[i]
            price = df_auction.current.values[i]
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['close'],count=5)
            if (price/df_price.close[-1] <g.auction_open_highlimit_1) and  (price/df_price.close[-1] >g.auction_open_lowlimit_1):
                g.buy_list.append(stockcode)
    
    if len(g.buy_list) ==0:
    #if len(g.buy_list) <=1: #测试
        log.info('今日无买信')
        return
    else:
        log.info('*****今日买信共%d只*****:' % len(g.buy_list))
        log.info(g.buy_list)
    
    #因信号收集屏蔽,回测打开
    total_value = context.portfolio.total_value
    buy_cash = round(0.5*total_value/len(g.buy_list))
    for stockcode in g.buy_list:
        if stockcode in list(context.portfolio.positions.keys()):
            continue
        buy_stock(context,stockcode,buy_cash)
    
    return

## 开盘时运行函数
def market_open(context):
    log.info('函数运行时间(market_open):'+str(context.current_dt.time()))
    today_date = context.current_dt.date()
    lastd_date = context.previous_date
    current_data = get_current_data()
    
    if len(g.buy_list) ==0:
        log.info('今日无买信')
        return
    else:
        log.info('*****今日买信共%d只*****:' % len(g.buy_list))
        log.info(g.buy_list)

    total_value = context.portfolio.total_value
    buy_cash = 0.5*total_value/len(g.buy_list)
    for stockcode in g.buy_list:
        if stockcode in list(context.portfolio.positions.keys()):
            continue
        buy_stock(context,stockcode,buy_cash)

## 收盘时运行函数
def market_run(context):
    log.info('函数运行时间(market_close):'+str(context.current_dt.time()))
    today_date = context.current_dt.date()
    lastd_date = context.previous_date
    current_data = get_current_data()
    
    #尾盘只卖
    hour = context.current_dt.hour
    minute = context.current_dt.minute
    
    for stockcode in context.portfolio.positions:
        if current_data[stockcode].paused == True:
            continue
        if context.portfolio.positions[stockcode].closeable_amount ==0:
            continue

        #非停出
        if current_data[stockcode].last_price != current_data[stockcode].high_limit:
            log.info('非涨停即出%s' % stockcode)
            sell_stock(context,stockcode,0)
            continue
        
## 收盘后运行函数
def after_market_close(context):
    log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
    g.trade_days = g.trade_days +1
    
    log.info(g.trade_days)
    log.info(g.open_num,g.open_nums_days)
    log.info(g.turn_num,g.turn_nums_days)


"""
---------------------------------函数定义-主要策略-----------------------------------------------
"""
 
#周期涨跌幅(1/-1)过滤
def get_rise_filter(context, stocklist, check_date, check_duration,rise_level,direction):
    # 输出运行时间
    log.info('-函数运行时间(get_rise_filter)：'+str(context.current_dt.time()))
    poollist =[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
    
    by_date = get_trade_days(end_date=check_date, count=check_duration)[0]

    h = get_price(security=stocklist, end_date=check_date, frequency='1d', fields=['close','paused'], count=check_duration, panel=False)
    if direction == -1:
        temp = h.groupby('code')['close'].apply(lambda x: x.values[-1]/x.values[-check_duration] <rise_level)
    elif direction == 1:
        temp = h.groupby('code')['close'].apply(lambda x: x.values[-1]/x.values[-check_duration] >rise_level)
        
    poollist = temp[temp].index.tolist()
    return poollist

#蒋的方法，N天M涨停过滤
def get_up_filter_jiang(context,stocklist,check_date,check_duration,up_num,direction):
    # 输出运行时间
    log.info('-函数运行时间(get_up_filter_jiang)：'+str(context.current_dt.time()))
    #0，预置，今天是D日
    all_data = get_current_data()
    poollist=[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
    
    # 交易日历
    trd_days = get_trade_days(end_date=check_date, count=check_duration)  # array[datetime.date]
    s_trd_days = pd.Series(range(len(trd_days)), index=trd_days)  # Series[index:交易日期，value:第几个交易日]
    back_date = trd_days[0]
    
    #2，形态过滤，一月内两次以上涨停(盘中过10%也算)
    start_time = time.time()
    # 取数
    df_price = get_price(stocklist,end_date=check_date,frequency='1d',fields=['pre_close','open','close','high','high_limit','low_limit','paused']
    ,skip_paused=False,fq='pre',count=check_duration,panel=False,fill_paused=True)
    
    # 过滤出涨停的股票，按time索引
    df_up = df_price[(df_price.close == df_price.high_limit) & (df_price.paused == 0)].set_index('time')
    # 标注出df_up中的time对应的是第几个交易日(ith)
    df_up['ith'] = s_trd_days
    
    code_set = set(df_up.code.values)
    if direction ==1:
        poollist =[stockcode for stockcode in code_set if ((len(df_up[df_up.code ==stockcode]) > up_num))]
    elif direction ==-1:
        poollist =[stockcode for stockcode in code_set if ((len(df_up[df_up.code ==stockcode]) < up_num))]
    else:
        poollist =[stockcode for stockcode in code_set if ((len(df_up[df_up.code ==stockcode]) == up_num))]
    
    end_time = time.time()
    #log.info('---%d天(%s--%s)%d次涨停过滤出%d只标的,构建耗时:%.1f 秒' % (check_duration,back_date,check_date,up_num,len(poollist),end_time-start_time))        
    #log.info(poollist)

    return poollist

#根据定义的选股条件选股
def get_rainbow_strong_filter(context,stocklist,check_date,strong_limit):
    log.info('-函数运行时间(get_rainbow_strong_filter)：'+str(context.current_dt.time()))
    all_data = get_current_data()
    poollist=[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
    # 取数
    df_price = get_price(stocklist,end_date=check_date,frequency='1d',fields=['pre_close','open','close','low','high','high_limit','low_limit','paused']
    ,skip_paused=False,fq='pre',count=1,panel=False,fill_paused=True)
    
    df_up = df_price[(df_price.close > df_price.open) & (df_price.close == df_price.high_limit) & (df_price.paused == 0)]
    up_list = df_up.code.values.tolist()
    print('---涨停板过滤后共%d只' % len(up_list))
    
    #排除一字板
    df_strong = df_price[(df_price.close != df_price.high_limit) & (df_price.close > strong_limit*df_price.pre_close) & (df_price.paused == 0)]
    strong_list = df_strong.code.values.tolist()
    print('---大阳过滤后共%d只' % len(strong_list))
    
    MA5 = MA(strong_list, check_date=check_date, timeperiod=5)
    MA10 = MA(strong_list, check_date=check_date, timeperiod=10)
    MA20 = MA(strong_list, check_date=check_date, timeperiod=20)

    strong_list = [stockcode for stockcode in strong_list if ((df_price[df_price.code == stockcode].low.values < MA5[stockcode] and df_price[df_price.code == stockcode
    ].close.values > MA5[stockcode]) or (df_price[df_price.code == stockcode].low.values < MA10[stockcode] and df_price[df_price.code == stockcode].close.values > MA10[
    stockcode]) or (df_price[df_price.code == stockcode].low.values < MA20[stockcode] and df_price[df_price.code == stockcode].close.values > MA20[stockcode]))]
    print('---均线过滤后共%d只' % len(strong_list))
    
    poollist = list(set(up_list+strong_list))
    print('---合并后共%d只' % len(poollist))
    
    return poollist
    
#根据定义的选股条件选股
#D1日高开套人(开0点上下落7点以上)+不能反包D日
def get_trap_filter(context,stocklist,check_date,trap_limit):
    log.info('-函数运行时间(get_trap_filter)：'+str(context.current_dt.time()))
    all_data = get_current_data()
    poollist=[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
    
    # 取数
    df_price = get_price(stocklist,end_date=check_date,frequency='1d',fields=['pre_close','open','close','low','high','high_limit','low_limit','paused']
    ,skip_paused=False,fq='pre',count=1,panel=False,fill_paused=True)
    
    df_trap = df_price[(df_price.open > df_price.pre_close) & (df_price.open > df_price.close) & (df_price.high/df_price.close >trap_limit) & (df_price.close/df_price.pre_close >0.93)]
    poollist = df_trap.code.values.tolist()
    
    return poollist
        
#针对输入进行基本面/量价面的过滤择优
def get_trap_optimize_filter(context,stocklist,check_date,filt_type):
    log.info('-函数运行时间(get_trap_optimize_filter)：'+str(context.current_dt.time()))
    all_data = get_current_data()
    poollist=[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return stocklist
        
    #静态，流通盘<40
    if filt_type =="S":
        df_value = get_valuation(stocklist, end_date=check_date, count=1, fields=['circulating_market_cap']) #先新后老
        df_value = df_value.dropna()
        #df_value.sort_values(by='circulating_market_cap', ascending=True, inplace=True)
        #cirm_list = list(df_value.code)[:int(0.6*len(stocklist))]
        poollist = list(df_value[df_value.circulating_market_cap < g.cirm_up].code)   #绝对
    elif filt_type == 'A':
        df_value = get_valuation(stocklist, end_date=check_date, count=1, fields=['circulating_market_cap']) #先新后老
        df_value = df_value.dropna()
        poollist = list(df_value[df_value.circulating_market_cap < g.cirm_up].code)   #绝对
        
        df_money = get_money_flow(poollist, end_date=check_date, count=1, fields=['sec_code','date','net_pct_main'])
        poollist = list(df_money[df_money.net_pct_main <-1].sec_code)
        
    return poollist
#去除T日是一字/T字/尾盘封板弱
def get_up_optimize_filter(context,stocklist):
    log.info('-函数运行时间(get_up_optimize_filter)：'+str(context.current_dt.time()))
    today_date = context.current_dt.date()
    lastd_date = context.previous_date
    all_data = get_current_data()
    poollist =[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
    
    #其他条件,在循环中过滤
    for stockcode in stocklist:
        #过滤掉一字板，T字板;
        df_lastd = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['open','close','high','high_limit','low_limit'],count=1)
        if (df_lastd['open'][0] == df_lastd['high_limit'][0] and df_lastd['close'][0] == df_lastd['high_limit'][0]):
            continue
        #过滤掉尾盘封板弱的;
        df_last30 = get_bars(stockcode, count=60, unit='1m', fields=['open','close','high','low'],include_now=True,df=True)
        if (df_last30['low'][:].min() != df_lastd['high_limit'][0]) & (df_last30['high'][:].max() == df_lastd['high_limit'][0]):
            continue
        """
        df_value = get_valuation(stockcode, end_date=lastd_date, count=1, fields=['circulating_market_cap','pe_ratio','pb_ratio']) #先新后老
        if len(df_value) ==0 or df_value['pe_ratio'].values[0] <0:  #存在数据缺失
            continue
        """
        poollist.append(stockcode)
    return poollist

##过滤N天内M倍最高量，X-买入前量能过滤，1X-为持仓的量能过滤
def get_highvolume_filter(context,stocklist,control_mode,check_dura,volume_ratio):
    log.info('-函数运行时间(get_highvolume_filter)：'+str(context.current_dt.time()))
    lastd_date = context.previous_date
    poollist =[]
    
    if len(stocklist)==0:
        log.info("输入为空")
        return poollist
        
    for stockcode in stocklist:
        if control_mode ==1:
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['volume'],count=check_dura)
            if df_price['volume'][-1] > volume_ratio*df_price['volume'].max():
                continue
            poollist.append(stockcode)
        elif control_mode ==2:
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['volume'],count=check_dura)
            if df_price['volume'][-1] > volume_ratio*df_price['volume'].mean():
                continue
            poollist.append(stockcode)
        elif control_mode ==3:
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['volume'],count=check_dura)
            if df_price['volume'][-1] > volume_ratio*df_price['volume'][-2]:
                continue
            poollist.append(stockcode)
        elif control_mode ==4:
            df_price = get_price(stockcode,end_date=lastd_date,frequency='daily',fields=['volume'],count=240)
            if df_price['volume'][-1] > 0.9*df_price['volume'].max():
                continue
            if df_price['volume'][-1] > 5*df_price['volume'][-20:].mean():
                continue
            poollist.append(stockcode)

    
    print('---量能控制%d-%d天放%.1f量过滤后共%d只' % (control_mode,check_dura,volume_ratio,len(poollist)))
    return poollist
    
#按比例过滤
def get_factor_filter_list(context,stock_list,jqfactor,sort,p1,p2):
    yesterday = context.previous_date
    score_list = get_factor_values(stock_list, jqfactor, end_date=yesterday, count=1)[jqfactor].iloc[0].tolist()
    df = pd.DataFrame(columns=['code','score'])
    df['code'] = stock_list
    df['score'] = score_list
    df = df.dropna()
    df.sort_values(by='score', ascending=sort, inplace=True)
    filter_list = list(df.code)[int(p1*len(stock_list)):int(p2*len(stock_list))]
    return filter_list

"""
---------------------------------函数定义-辅助函数-----------------------------------------------
"""
##买入函数
def buy_stock(context,stockcode,cash):
    today_date = context.current_dt.date()
    current_data = get_current_data()
    
    if stockcode[0:2] == '68':
        last_price = current_data[stockcode].last_price
        if order_target_value(stockcode,cash,MarketOrderStyle(1.1*last_price)) != None: #科创板需要设定限值
            log.info('%s买入%s' % (today_date,stockcode))
    else:
        if order_target_value(stockcode, cash) != None:
            log.info('%s买入%s' % (today_date,stockcode))
            
##卖出函数
def sell_stock(context,stockcode,cash):
    today_date = context.current_dt.date()
    current_data = get_current_data()
    
    if stockcode[0:2] == '68':
        last_price = current_data[stockcode].last_price
        if order_target_value(stockcode,cash,MarketOrderStyle(0.9*last_price)) != None: #科创板需要设定限值
            log.info('%s卖出%s' % (today_date,stockcode))
    else:
        if order_target_value(stockcode,cash) != None:
            log.info('%s卖出%s' % (today_date,stockcode))