量化进阶——量化交易策略之羊驼和均线策略

阅读原文：http://club.jr.jd.com/quant/topic/1425690

京东金融官方资讯QQ群:456448095 有什么想咨询的都可以来询问我们

相当于加了一个开关，站在40日均线上，就执行羊驼策略，否则，就空仓。

源码：

import random

import numpy as np

import pandas as pd

from pandas import Series,DataFrame

import scipy.stats as stats

import math

# 设置股票池，本程序中为所有沪深300的股票

stocks = get_index_stocks('000300.XSHG')

#求出股票池中有多少股票

num=len(stocks)

set_universe(stocks)

#设置benchmark，默认为沪深300

#set_benchmark('510050.XSHG')

#设置回测条件

set_commission(PerTrade(buy_cost=0.0008, sell_cost=0.0015, min_cost=5))

set_slippage(FixedSlippage(0))

#设置初始买入多少只股票

num_of_stocks=10

#设置每次更新时替换多少只股票

num_of_change=3

#设置计算几日收益率

period=2

#用一个列表来保存每天持有的股票代码

stockshold=[]

#判断参数输入是否符合条件，如果不符合，则重置为默认值

if num_of_stocks>num:

log.info('too large num_of_stocks')

num_of_stocks=10

elif num_of_change>num_of_stocks:

log.info('too large num_of_change')

num_of_change=1

num_MA=40

security = '000300.XSHG'

#预处理数据，将没有数据的股票剔除，同时加入收益率

#构成一个列索引为股票名，收益率一行的索引为

#'return'的dataframe，并返回这个dataframe

def process():

#取出每只股票period天的收盘价格

stocks_info=history(period,'1d','close')

#去除信息不全的数据

stocks_info.dropna(axis=0,how='any',thresh=None)

#取出昨天和period天之前的收盘价，计算收益率

a1=list(stocks_info.iloc[0])

a2=list(stocks_info.iloc[period-1])

a1=np.array(a1)

a2=np.array(a2)

#用一个dataframe来保存所有股票的收益率信息

stocks_return=DataFrame(a2/a1,columns=['return'],index=stocks_info.columns)

stocks_info=stocks_info.T

#把收益率的数据加到相应的列

stocks_info=pd.concat([stocks_info,stocks_return],axis=1)

#将股票信息按照收益率从大到小来存储

stocks_info=stocks_info.sort(columns=['return'],ascending=[False])

#返回处理好的dataframe

return stocks_info#股票入池

def BuyStocks(stocks_info,cash):

#计算现在持有的股票数

current_num=len(stockshold)

stocks_info=stocks_info.T

#将已持有的股票从股票池中剔除

for i in range(0,current_num):

if stockshold[i] in stocks_info.columns:

del stocks_info[stockshold[i]]

stocks_info=stocks_info.T

#计算在每只股票上可以支付的现金

cash=cash/num_of_stocks-current_num

for i in range(0,num_of_stocks-current_num):

#取得股票当前的价格

current_price=stocks_info['current_price'][i]

#判断是否有价格数据

if math.isnan(current_price)==False:

#计算可以每只股票可以购买的数量

num_of_shares=int(cash/current_price)

if num_of_shares>0:

order(stocks_info.index[i],+num_of_shares)

log.info('buying %s' %(stocks_info.index[i]))

#将购买的股票代码加到stockhold中

stockshold.append(stocks_info.index[i])

#股票出池

def SellStocks(stocks_info,num_of_change):

stocks_hold=DataFrame()

current_num=len(stockshold)

'log.info('here' % (current_num))'

#用一个dataframe来保存持有的股票的信息

for i in range(0,current_num):

stocks_hold=pd.concat([stocks_hold,stocks_info.loc[stockshold[i]]],axis=1)

stocks_hold=stocks_hold.T

#在持有的股票数不为0时，将持有的股票信息按照收益率大小从小到大排序

if current_num>0:

stocks_hold=stocks_hold.sort(columns=['return'])

#把收益率最低的股票卖空

for k in range(0,min(num_of_change,current_num)):

log.info(num_of_change)

#判断是否停牌

if stocks_hold['paused'][k]=='False':

order_target(stocks_hold.index[k],0)

log.info('Selling %s' % (stocks_hold.index[k]))

#在stockshold中去除已经卖空的股票的信息

stockshold.remove(stocks_hold.index[k])

# 每个单位时间(如果按天回测,则每天调用一次,如果按分钟,则每分钟调用一次)调用一次

def handle_data(context, data):

stocks_info=process()

stocks_num=len(stocks_info.index)

#用一个列表来保存所有股票是否停牌的信息

pause=[]

for i in range(0,stocks_num):

if data[stocks_info.index[i]].paused==True:

pause.append('True')

else:

pause.append('False')

#将列表转换成dataframe以便加入到stocks_info中

paused=DataFrame(pause,columns=['paused'],index=stocks_info.index)

stocks_info=pd.concat([stocks_info,paused],axis=1)

#用一个列表来保存所有股票当前的价格信息

currentprice=[]

for i in range(0,stocks_num):

currentprice.append(data[stocks_info.index[i]].price)

current_price=DataFrame(currentprice,columns=['current_price'],index=stocks_info.index)

#将股票是否停牌，当前价格的信息添加到stocks_info中

stocks_info=pd.concat([stocks_info,current_price],axis=1)

#取得当前现金

cash=context.portfolio.cash

#判断MA的条件

close_price =history(num_MA,'1d', 'close', ['000300.XSHG'])

Closemean=close_price[security].mean()

# 取得当前价格

Current_close = close_price.iloc[-1,-1]

log.info('close %s' % (Current_close))

log.info('mean %s' % Closemean)

if Current_close > Closemean:

#执行卖出股票的函数

SellStocks(stocks_info,3)

#执行买入股票的函数

BuyStocks(stocks_info,cash)

elif Current_close <>

SellStocks(stocks_info,10)

阅读原文：http://club.jr.jd.com/quant/topic/1425690