# 导入函数库
from jqdata import *
import datetime
# 初始化函数，设定基准等等
def initialize(context):
set_benchmark('000300.XSHG') # 设定沪深300作为基准
set_option('use_real_price', True) # 开启动态复权模式(真实价格)
set_option("match_by_signal", True) # # 强制撮合，仅支持限价单。使用限价单进行委托时将不对委托价格和成交数量进行任何检查而直接成交
set_option("avoid_future_data", True) # 避免未来数据,在Python2回测时需注释掉
log.set_level('order', 'error') # 过滤掉order系列API产生的比error级别低的log
### 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='before_open', reference_security='000300.XSHG')
# 开盘时或每分钟开始时运行
run_daily(market_open, time='every_bar', reference_security='000300.XSHG')
# 收盘后运行
run_daily(after_market_close, time='after_close', reference_security='000300.XSHG')
g.pool=[]
g.max_holding=2
## 开盘前运行函数
def before_market_open(context):
g.pool=select(context)
## 开盘时运行函数
def market_open(context):
pass
## 收盘后运行函数
def after_market_close(context):
pass
def handle_data(context, data):
cur_data=get_current_data()
if(1455 > int(context.current_dt.strftime("%H%M")) > 925):
if cur_data['000300.XSHG'].last_price>cur_data['000300.XSHG'].day_open:
toBuy(context);
if(int(context.current_dt.strftime("%H%M")) ==925):
toSell(context);
def toSell(context):
curr_data = get_current_data()
hour = context.current_dt.hour
minute = context.current_dt.minute
for stock in context.portfolio.positions:
closeable_amount = context.portfolio.positions[stock].closeable_amount
if closeable_amount > 0 and curr_data[stock].last_price < curr_data[stock].high_limit: # 尾盘未涨停
order_target(stock, 0) # 卖出
log.info("卖出： %s %s" % (curr_data[stock].name, stock))
def toBuy(context):
if len(g.pool)==0:
return
curr_data = get_current_data()
if(len(context.portfolio.positions)< g.max_holding):
per_cash=context.portfolio.available_cash/(g.max_holding-len(context.portfolio.positions))
for stock in g.pool:
if stock in context.portfolio.positions:
continue
pre_close=attribute_history(stock, 1, '1d', ('close'))['close'][-1]
if(len(context.portfolio.positions)< g.max_holding):
if get_current_data()[stock].last_price>pre_close*1.095:
order_target_value(stock, per_cash)
log.info("买入： %s %s" % (curr_data[stock].name, stock))
## 选股
def select(context):
endDate=context.previous_date
trd_days = get_trade_days(end_date=endDate, count=365)
security_list = get_all_securities('stock', trd_days[0]).index.tolist()
stk_list=[s for s in security_list if not (s[:2]=='30' or s[:2]=='68')]
p=get_price(stk_list, count = 60, end_date=endDate, frequency='daily', fields=['close','pre_close','high_limit'])
df_ZT=(p['close'].iloc[-1]==p['high_limit'].iloc[-1]) & (p['high_limit'].iloc[-1]/p['pre_close'].iloc[-1]>1.09)
up_limit_list=list(df_ZT[df_ZT==True].index)
df_250=(p['close'].iloc[-1]==p['close'].max())
up_max250_list=list(df_250[df_250==True].index)
temp_list=[]
if len(up_limit_list)/len(security_list)>0.001:
temp_list= list(set(up_limit_list).intersection(up_max250_list))
print(temp_list)
return temp_list
##
2025-01-29
雪球
