98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
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# coding: utf-8
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# In[ ]:
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# 导入函数库
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import jqdata
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import pandas as pd
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import numpy as np
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# 初始化函数，设定基准等等
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def initialize(context):
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# 设定沪深300作为基准
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set_benchmark('000300.XSHG')
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# 开启动态复权模式(真实价格)
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set_option('use_real_price', True)
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# 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
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set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
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#获取沪深300股票池
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stock_set=get_index_stocks('000300.XSHG')
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#此处可增加选股条件
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q = query(
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valuation.code, # 股票代码
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).filter(
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valuation.code.in_(stock_set),#只对设定股票池执行
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)
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fdf = get_fundamentals(q)
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#取前50只股
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fdf=fdf.head(50)
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#获取股票列表
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stock_list=list(fdf['code'])
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trend={}
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for security in stock_list:
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trend[security]='None'
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def handle_data(context, data):
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N = 20 # 计算TR时的N
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M = 49 # 计算MATRIX时的M
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num=3
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length_of_data = N+M+num # 取closeprice的天数，为了足够计算MATRIX、TRIX
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cash=context.portfolio.available_cash/50
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for security in stock_list:
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close_price=attribute_history(security,length_of_data,'1d',('close'),skip_paused=True)
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where_are_nan = np.isnan(close_price)
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where_are_inf = np.isinf(close_price)
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close_price[where_are_nan] = 0
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close_price[where_are_inf] = 0
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MA5=close_price['close'][-5:].mean()
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MA10=close_price['close'][-10:].mean()
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ma5=close_price['close'][-6:-1].mean()
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ma10=close_price['close'][-11:-1].mean()
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price_array={}
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for i in range(0,M+num):
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price_array[i]=close_price['close'][i:i+N]
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#TR=收盘价的N日指数移动平均
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TR={}
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for i in range(0,M+num):
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TR[i]=np.mean(price_array[i])
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#TRIX=(TR-昨日TR)/昨日TR*100
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TRIX={}
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for i in range(1,M+num):
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if TR[i]==0:
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TRIX[i]=0
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continue
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TRIX[i]=(TR[i]-TR[i-1])/TR[i]*100
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#MATRIX=TRIX的M日简单移动平均
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MATRIX={}
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for i in range(0,num):
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TRIX_sum=0
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for j in range(1,M):
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TRIX_sum=TRIX_sum+TRIX[i+j]
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MATRIX[i]=TRIX_sum/M
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current_price=data[security].price
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length=0
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for i in range(0,num-1):
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if TRIX[M+i]>MATRIX[i]:
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length=length+1
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else:
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length=length-1
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if length>0 and MATRIX[num-1]>MATRIX[0]:
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trend[security]='up'
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elif length<0 and MATRIX[num-1]<MATRIX[0]:
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trend[security]='down'
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if trend[security]=='up':
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order_value(security,cash)
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elif trend[security]=='down' and security in context.portfolio.positions:
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if MA5<MA10:
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order_target(security,0)
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elif MA5>MA10 and ma5<ma10 :
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order_value(security,cash)
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