# 布林带回调系统-日内 > 来源：https://uqer.io/community/share/566929a4f9f06c6c8a91b6e6 ```py import numpy as np import pandas as pd from pandas import DataFrame start = '2014-01-01' # 回测起始时间 end = '2015-01-01' # 回测结束时间 benchmark = 'HS300' # 策略参考标准 universe = set_universe('HS300') # 证券池，支持股票和基金 capital_base = 100000 # 起始资金 freq = 'm' # 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 = 239 # 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 period = 10 multiple=1.5 threshold=-0.1 boll=pd.DataFrame(index=universe,columns = ['mean_cp','high_channel','low_channel']) 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 if(account.current_minute=='09:30'): close_prices = account.get_daily_attribute_history('closePrice', period) for s in account.universe: mean_cp = close_prices[s].mean() bias = multiple*np.std(close_prices[s]) high_channel = mean_cp + bias low_channel = mean_cp - bias boll.at[s,'high_channel']=high_channel boll.at[s,'low_channel']=low_channel boll.at[s,'mean_cp']=mean_cp elif(account.current_minute=='14:50'): print account.current_date,",",account.valid_secpos else: for s in account.valid_secpos: #清仓 if account.referencePrice[s]>=boll.at[s,'mean_cp'] : order_to(s, 0) buylist=[] c = account.referencePortfolioValue for s in account.universe: if ((account.referencePrice[s]-boll.at[s,'low_channel'])/boll.at[s,'low_channel'])<=threshold: buylist.append(s) if (len(buylist)==0): return else: w=min(0.2,1.0/len(buylist))# 最大仓位1/5 for s in buylist: p=account.referencePrice[s]*1.01 num=int(c * w / p) order(s, num) ```