盈利预增事件

发布于 2022-02-20 22:26:14 字数 18189 浏览 902 评论 0 收藏 0

每次建仓等权重买入上季度净利润预增(盈利且盈利增加)的股票

import pandas as pd
import numpy as np
from CAL.PyCAL import *
from pandas import DataFrame,Series
from datetime import datetime, timedelta
start = '2013-01-01'                       # 回测起始时间
end = (datetime.today() - timedelta(days=1)).strftime('%Y%m%d')  # 截止日期
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')             # 证券池，支持股票和基金
capital_base = 1000000                     # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 63                          # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟
cal = Calendar('China.SSE')

def initialize(account):                   # 初始化虚拟账户状态
    pass

def handle_data(account):                  # 每个交易日的买入卖出指令

    today = account.current_date.strftime('%Y%m%d')
    yesterday = cal.advanceDate(account.current_date, '-1B', BizDayConvention.Following).strftime('%Y%m%d')
    yester_refresh_day = cal.advanceDate(account.current_date, '-62B' , BizDayConvention.Following).strftime('%Y%m%d')
    total_money = account.referencePortfolioValue    
    prices = account.referencePrice     
    # 去除新上市或复牌的股票
    opn = account.get_attribute_history('openPrice', 1)
    account.universe = [s for s in account.universe if not (np.isnan(opn.get(s, 0)[0]) or opn.get(s, 0)[0] == 0 )]

    buylist =[]
    for s in  account.universe : 
        try : 
            temp=DataAPI.FdmtEfGet(secID = s,forecastType ='22',publishDateBegin= yester_refresh_day , publishDateEnd = yesterday,field=['secID','publishDate','NIncAPChgrLL', 'NIncAPChgrUPL'],pandas="1")
            buylist.append(s)
        except : 
            continue    
    sell_list = [x for x in account.valid_secpos if x not in buylist]    
    for s in sell_list :
        order_to(s,0)            
    for s in buylist :  
        order_to(s, int(total_money*0.99/len(buylist)/prices[s]/100)*100)

# 统计代码 不用看啰
from pandas import DataFrame
data = DataFrame()
for s in range(len(set_universe('A'))/100 + 1) :        
    if s == len(set_universe('A'))/100 :
        temp_list = set_universe('A')[s*100:]
    else :
        temp_list = set_universe('A')[s*100:(s+1)*100] 
    try:
        if not temp_list == 0 :    
            data_temp = DataAPI.FdmtEfGet(secID = temp_list,field=['secID','publishDate','NIncAPChgrLL', 'NIncAPChgrUPL'],pandas="1")
    except : 
        print '错误！'          
    data = pd.concat([data,data_temp]) 
data['publishDate'] = pd.to_datetime(data['publishDate']) 
list1 = [] 
data07 = data[data['publishDate'] < '20080101']
data07.drop_duplicates('secID' , inplace = True)
list1.append(len(data07))
data08 = data[(data['publishDate'] < '20090101') & (data['publishDate'] >= '20080101')]
data08.drop_duplicates('secID' , inplace = True)
list1.append(len(data08))
data09 = data[(data['publishDate'] < '20100101') & (data['publishDate'] >= '20090101')]
data09.drop_duplicates('secID' , inplace = True)
list1.append(len(data09))
data10 = data[(data['publishDate'] < '20110101') & (data['publishDate'] >= '20100101')]
data10.drop_duplicates('secID' , inplace = True)
list1.append(len(data10))
data11 = data[(data['publishDate'] < '20120101') & (data['publishDate'] >= '20110101')]
data11.drop_duplicates('secID' , inplace = True)
list1.append(len(data11))
data12 = data[(data['publishDate'] < '20130101') & (data['publishDate'] >= '20120101')]
data12.drop_duplicates('secID' , inplace = True)
list1.append(len(data12))
data13 = data[(data['publishDate'] < '20140101') & (data['publishDate'] >= '20130101')]
data13.drop_duplicates('secID' , inplace = True)
list1.append(len(data13))
data14 = data[(data['publishDate'] < '20150101') & (data['publishDate'] >= '20140101')]
data14.drop_duplicates('secID' , inplace = True)
list1.append(len(data14))
data15 = data[(data['publishDate'] < '20160101') & (data['publishDate'] >= '20150101')]
data15.drop_duplicates('secID' , inplace = True)
list1.append(len(data15))

data2 = DataFrame()
for s in range(len(set_universe('A'))/100 + 1) :        
    if s == len(set_universe('A'))/100 :
        temp_list = set_universe('A')[s*100:]
    else :
        temp_list = set_universe('A')[s*100:(s+1)*100] 
    try:
        if not temp_list == 0 :    
            data_temp = DataAPI.FdmtEfGet(secID = temp_list,forecastType= '22' ,field=['secID','publishDate','NIncAPChgrLL', 'NIncAPChgrUPL'],pandas="1")
    except : 
        print '错误！'          
    data2 = pd.concat([data2,data_temp]) 
data2['publishDate'] = pd.to_datetime(data2['publishDate'])
list2 = [] 
data07 = data2[data2['publishDate'] < '20080101']
data07.drop_duplicates('secID' , inplace = True)
list2.append(len(data07))
data08 = data2[(data2['publishDate'] < '20090101') & (data2['publishDate'] >= '20080101')]
data08.drop_duplicates('secID' , inplace = True)
list2.append(len(data08))
data09 = data2[(data2['publishDate'] < '20100101') & (data2['publishDate'] >= '20090101')]
data09.drop_duplicates('secID' , inplace = True)
list2.append(len(data09))
data10 = data2[(data2['publishDate'] < '20110101') & (data2['publishDate'] >= '20100101')]
data10.drop_duplicates('secID' , inplace = True)
list2.append(len(data10))
data11 = data2[(data2['publishDate'] < '20120101') & (data2['publishDate'] >= '20110101')]
data11.drop_duplicates('secID' , inplace = True)
list2.append(len(data11))
data12 = data2[(data2['publishDate'] < '20130101') & (data2['publishDate'] >= '20120101')]
data12.drop_duplicates('secID' , inplace = True)
list2.append(len(data12))
data13 = data2[(data2['publishDate'] < '20140101') & (data2['publishDate'] >= '20130101')]
data13.drop_duplicates('secID' , inplace = True)
list2.append(len(data13))
data14 = data2[(data2['publishDate'] < '20150101') & (data2['publishDate'] >= '20140101')]
data14.drop_duplicates('secID' , inplace = True)
list2.append(len(data14))
data15 = data2[(data2['publishDate'] < '20160101') & (data2['publishDate'] >= '20150101')]
data15.drop_duplicates('secID' , inplace = True)
list2.append(len(data15))

红柱表示年间发布过业绩预告的公司数量
黄柱表示年间发布过盈利预增的公司数量

# plot Statistics
import numpy as np
import matplotlib.pyplot as plt
N = 9
ind = np.arange(N) 
width = 0.35      
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(211)
rects1 = ax.bar(ind, list1, width, color='r')
rects2 = ax.bar(ind+width, list2, width, color='y')
# add some
ax.set_ylabel('Number')
ax.set_title('Statistics A shares')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('2007', '2008', '2009', '2010', '2011','2012','2013','2014','2015/11') )
ax.legend((rects1[0], rects2[0]), ('ALL', 'Profit_inc') , loc = '1')
def autolabel(rects):
    # attach some text labels
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),
                ha='center', va='bottom') 
autolabel(rects1)
autolabel(rects2)
plt.show()

构造一个事件驱动策略看下盈利预增效应是否长期有效
策略思想：以企业净利润预增预报为事件驱动，卖出同期持仓（上个换仓期到本次换仓）中收益率最low的股票，买入盈利预增的股票。
回测区间： 07年 —— 今

import pandas as pd
import numpy as np
from CAL.PyCAL import *
from pandas import DataFrame,Series
from datetime import datetime, timedelta
start = '20070801'                       # 回测起始时间
end = (datetime.today() - timedelta(days=1)).strftime('%Y%m%d')  # 截止日期

benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')           # 证券池，支持股票和基金
capital_base = 1000000                     # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 1                           # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟
cal = Calendar('China.SSE')

def initialize(account):                   # 初始化虚拟账户状态
    account.last_refreshtime = start

def handle_data(account):                  # 每个交易日的买入卖出指令

    today = account.current_date.strftime('%Y%m%d')
    yesterday = cal.advanceDate(account.current_date, '-1B', BizDayConvention.Following).strftime('%Y%m%d')
    last_day  = (account.current_date - timedelta(days=1)).strftime('%Y%m%d')
    yester_refresh_day = cal.advanceDate(account.current_date, '-62B' , BizDayConvention.Following).strftime('%Y%m%d')
    total_money = account.referencePortfolioValue    
    prices = account.referencePrice 
    buylist =[]   
    # 去除新上市或复牌的股票
    opn = account.get_attribute_history('openPrice', 1)
    account.universe = [s for s in account.universe if not (np.isnan(opn.get(s, 0)[0]) or opn.get(s, 0)[0] == 0 )]

    # 初始建仓(选当前净利润最高的20只股票)：
    if len(account.valid_secpos) == 0 : 
        # 净利润增长率
        NetProfitGrowRate = DataAPI.MktStockFactorsOneDayGet(tradeDate=yesterday,secID=account.universe,field=u"secID,NetProfitGrowRate",pandas="1")
        NetProfitGrowRate = NetProfitGrowRate.sort('NetProfitGrowRate',ascending = False).drop_duplicates('secID')
        buylist = list(NetProfitGrowRate['secID'].values[0:20])
        for s in buylist :  
            order_to(s, int(total_money*0.99/len(buylist)/prices[s]/100)*100)
        account.last_refreshtime = today
        return

    # 获取业绩预增的股票,最多取20只
    try : 
        temp = DataAPI.FdmtEfGet(secID = account.universe , forecastType ='22',publishDateBegin= yesterday , publishDateEnd = last_day , field=['secID','publishDate','NIncAPChgrLL', 'NIncAPChgrUPL'],pandas="1")
        temp['meanGrowRate'] = (temp['NIncAPChgrLL'] + temp['NIncAPChgrUPL']) / 2
        temp.sort('meanGrowRate', ascending=False).drop_duplicates('secID' ,inplace = True)        
        buylist = list(temp['secID'].values[0:20])
    except : 
        return 

    change_stock = [x for x in account.valid_secpos if x not in buylist] 
    buylist = [x for x in buylist if x not in account.valid_secpos]
    # 换仓     
    price1 = DataAPI.MktEqudAdjGet(secID=change_stock, tradeDate=account.last_refreshtime , field=u"secID,openPrice",pandas="1")
    price2 = DataAPI.MktEqudAdjGet(secID=change_stock, tradeDate=yesterday , field=u"secID,closePrice",pandas="1")
    # 计算持仓股这段时间的涨幅
    price1['stock_returns'] = price2['closePrice'] / price1['openPrice']
    price1.sort('stock_returns',ascending = True,inplace = True)
    # 剔除上个换仓日到现在最挫的股票 ：
    sell_list = price1['secID'].values[0:len(buylist)]
    for s in sell_list : 
        account.cash += prices[s] * account.valid_secpos.get(s)
        order_to(s,0)            
    for s in buylist :  
        order(s, int(account.cash / len(buylist)/prices[s]/100)*100)
    #更变最新调仓日期   
    account.last_refreshtime = today