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# 原文一般包含策略说明，如有疑问建议到原文和作者交流讨论。
# 克隆自聚宽文章：https://www.joinquant.com/post/25698
# 标题：均线黏合，突破前三十个交易日最高点选股法
# 作者：fireflytxy
# 克隆自聚宽文章：https://www.joinquant.com/post/21474
# 标题：基于分析师深度研报的T+1策略-年化51%，回撤3.7%
# 作者：wsd518
# 导入函数库
from jqdata import *
import jqdata
import numpy as np
import pandas as pd
import datetime
def get_stocks_tobuy(context):
date1= str(context.current_dt)[0:10]
#print(date1)
stock_list1 = get_index_stocks('000001.XSHG')+get_index_stocks('399106.XSHE')
#stock_list1 = delisted_filter(stock_list1)
stock_list1 = delisted_filter(stock_list1)
stock_list1 = st_filter(stock_list1)
df = get_fundamentals(query(
valuation.code, valuation.market_cap,income.total_operating_revenue
).filter(
valuation.circulating_market_cap > 20,
valuation.pe_ratio > 0,
valuation.code.in_(stock_list1)
).order_by(
# 按市值降序排列
valuation.market_cap.desc()
) )
stock_list1 = df.code.tolist()
close = get_price(stock_list1, end_date= date1,
frequency='daily', fields='close', count=250)['close']
close_12days = get_price(stock_list1, end_date= date1,
frequency='daily', fields='close', count=12)['close'].iloc[-30:1,:]
Open = get_price(stock_list1, end_date= date1,
frequency='daily', fields='open', count=1)['open'].T
Open.columns=['open']
close13 = close.rolling(13).mean().iloc[-1,:].T
close34 = close.rolling(34).mean().iloc[-1,:].T
close55 = close.rolling(55).mean().iloc[-1,:].T
close13_1 = close.rolling(13).mean().iloc[-10,:].T
close34_1 = close.rolling(34).mean().iloc[-10,:].T
close55_1 = close.rolling(55).mean().iloc[-10,:].T
close13_2 = close.rolling(13).mean().iloc[-19,:].T
close34_2 = close.rolling(34).mean().iloc[-19,:].T
close55_2 = close.rolling(55).mean().iloc[-19,:].T
#close1 = close.iloc[-3,:].T
#close2 = close.iloc[-4,:].T
###55日线开始向上拐头
#close55pre = close.rolling(55).mean().iloc[-10,:].T
close250 = close.rolling(250).mean().iloc[-1,:].T
highest = close_12days.max()
lowest = close_12days.min()
All = pd.concat([close.iloc[-1,:],Open,highest,lowest,close13,close34,close55,close250
,close13_1,close34_1,close55_1,close13_2,close34_2,close55_2],axis = 1)
All.columns = ['close','open','highest','lowest','close13','close34','close55','close250',
'close13_1','close34_1','close55_1','close13_2','close34_2','close55_2'
]
#print(All.head() )
stock_list = stock_list = All[ ( (All['highest']- All['lowest'])/All['close'] < 0.2 )
& (All['close'] > All['highest']) & (All['close13'] > All['close34']) &
(All['close34'] > All['close55']) & (All['close'] >= All['open'])
& ( abs( ( All['close34_1'] - All['close13_1'])/All['close13_1'] ) <= 0.03 )
& ( abs( ( All['close55_1'] - All['close34_1'])/All['close34_1'] ) <= 0.03 )
& ( abs( ( All['close55_1'] - All['close13_1'])/All['close13_1'] ) <= 0.03 )
& ( abs( ( All['close34_2'] - All['close13_2'])/All['close13_2'] ) <= 0.03 )
& ( abs( ( All['close55_2'] - All['close34_2'])/All['close34_2'] ) <= 0.03 )
& ( abs( ( All['close55_2'] - All['close13_2'])/All['close13_2'] ) <= 0.03 )
]
df = [x for x in stock_list.index.tolist() if x not in g.previous_buylist.keys()]
#print(stock_list)
return df
# 过滤退市
def delisted_filter(stock_list):
current_data = get_current_data()
stock_list = [stock for stock in stock_list if not '退' in current_data[stock].name]
return stock_list
# 过滤ST
def st_filter(stock_list):
current_data = get_current_data()
stock_list = [stock for stock in stock_list if not current_data[stock].is_st]
return stock_list
## 收盘后运行函数
def after_trading_end(context):
log.info(str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
#控制突破30日高点后只有第一次买入
for stock in list(g.previous_buylist.keys()):
if g.previous_buylist[stock] >=30:
del g.previous_buylist[stock]
else:
g.previous_buylist[stock]+=1
stock_list = get_stocks_tobuy(context)
to_buy = stock_list
g.my_stock_today = to_buy
if len(to_buy)>0:
print(to_buy)
temp = []
for stock in context.portfolio.positions.keys():
temp.append(stock)
#print('type of temp',type(temp))
#print(temp)
if len(temp)>0:
date1= str(context.current_dt)[0:10]
close = get_price(temp , end_date= date1,
frequency='daily', fields='close', count=16)['close']
close13_1 = close.rolling(13).mean().iloc[-1,:].T
close13_2 = close.rolling(13).mean().iloc[-2,:].T
close13_3 = close.rolling(13).mean().iloc[-3,:].T
close_1 = close.iloc[-1,:].T
close_2 = close.iloc[-2,:].T
close_3 = close.iloc[-3,:].T
stock_hold = pd.concat([close_1,close_2,close_3,close13_1,close13_2,close13_3],axis = 1)
stock_hold.columns = ['close1','close2','close3','close13_1','close13_2','close13_3']
to_sell = stock_hold[ (stock_hold['close1'] < stock_hold['close13_1'])
& (stock_hold['close2'] < stock_hold['close13_2']) & (stock_hold['close3'] < stock_hold['close13_3'])]
if len(to_sell)>0:
g.stock_tosell = to_sell.index.tolist()
# 初始化函数，设定基准等等
def initialize(context):
#body=read_file("深度报告数据0.csv")
#g.datapd=pd.read_csv(BytesIO(body))
g.stock_tosell = []
g.previous_buylist = {}
g.my_stock_today = []
#g.datapd['证券代码']=[str(1000000+int(code))[1::] for code in g.datapd['证券代码'].tolist()]
#去除新三板股票
#g.datapd=g.datapd[g.datapd['证券代码']<'7']
#g.datapd=g.datapd[(g.datapd['证券代码']<'4')|(g.datapd['证券代码']>='6')]
#g.datapd['证券代码']=[code+'.XSHG' if code[0]=='6' else code+'.XSHE' for code in g.datapd['证券代码'].tolist()]
#留下14：59前分享的研报
#g.datapd=g.datapd[g.datapd['time']<='14:59']
#g.datapd=g.datapd[g.datapd['页数']>=20]
# print(g.datapd.head(10))
# raise 11
# 设定沪深300作为基准
set_benchmark('000300.XSHG')
# 开启动态复权模式(真实价格)
set_option('use_real_price', True)
# 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')
# 过滤掉order系列API产生的比error级别低的log
# log.set_level('order', 'error')
### 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, open_commission=0, close_commission=0, min_commission=0), 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）
#中午卖出股
run_daily(buy_stock, time='09:31', reference_security='000300.XSHG')
# 收盘前买入股票
#run_daily(buy_stock, time='15:00', reference_security='000300.XSHG')
# 清空卖出所有持仓
def sell_stock(context):
#print(g.previous_stocklist)
for stock in g.stock_tosell:
order_target_value(stock,0)
g.stock_tosell = []
#for stock in context.portfolio.positions.keys():
# if stock not in g.previous_stocklist.keys():
# order_target_value(stock,0)
return
def buy_stock(context):
#date1=str(context.current_dt)[0:10]
#date2=str(context.previous_date)[0:10]
#datapd2=g.datapd[g.datapd['分享日期']==date1]
#buylist=list(set(datapd2['证券代码'].tolist()))
sell_stock(context)
buynum = len(context.portfolio.positions.keys()) + len(g.my_stock_today)#len(g.my_stock_today);
if len(context.portfolio.positions.keys())<=6: # >0:
if len(g.my_stock_today)>0:
print('今天要买的股票是%s'%(",".join(g.my_stock_today)) )
buynum =6
target_to_buy = context.portfolio.total_value/buynum
for stock in g.my_stock_today:
cash = context.portfolio.available_cash
if cash/target_to_buy<0.1:
break;
order_target_value(stock,target_to_buy)
g.previous_buylist[stock] = 0
#g.previous_stocklist[stock] = 0;
g.my_stock_today = []
return
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