失眠网 > python量化策略——多均值-趋势-股债轮动-策略

python量化策略——多均值-趋势-股债轮动-策略

时间：2018-09-28 01:09:25

考虑两种资产，股票和债券。根据星号轮动配置。构建多个动量，当同时满足时，买入信号（股票）读取数据，并计算t1、t2、t3、t4和t5天的均值，if DF[i]>nmean3[i] and DF[i]>nmean4[i] and DF[i]>n*mean5[i] 则买入股票，else:买入债券

运行下程序，需要获取财经数据库的token码，这里获取token

"""@dazip"""import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom datetime import datetime, datefrom statsmodels.regression import linear_modelimport statsmodels.api as smimport threadingfrom queue import Queueimport mathimport tushare as tsimport matplotlibimport talibimport seaborn as sns sns.set(style="darkgrid", palette="muted", color_codes=True) from scipy import stats,integrate%matplotlib inline sns.set(color_codes=True)matplotlib.rcParams['axes.unicode_minus']=Falseplt.rcParams['font.sans-serif'] = ['SimHei'] # 中文显示plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号code1="000001.SH"code2="000012.SH"#freq=65def momentum(freq=65,test_start="0101",test_end="2001",t1=5,t2=10,t3=15,t4=20,t5=25,n=1):#读取数据 def dataread():ts.set_token('toke码')#需要获取token码https://tushare.pro/register?reg=385920pro = ts.pro_api()df_stock=pro.index_daily(ts_code=code1, start_date=test_start, end_date=test_end, fields='close,trade_date')df_bond=df=pro.index_daily(ts_code=code2,start_date=test_start, end_date=test_end , fields='trade_date,close')return df_stock,df_bonddf_stock,df_bond=dataread() #计算均值，时间为t1 t2 t3 t4def mean(t):df_stock.index=pd.to_datetime(df_stock.trade_date)return df_stock.close.sort_index().rolling(window=t).mean()def ret_base():df_stock.index=pd.to_datetime(df_stock.trade_date)df_bond.index =pd.to_datetime(df_bond.trade_date)ret_stock=(df_stock.close-df_stock.close.shift(-1))/df_stock.close.shift(-1)ret_bond= (df_bond.close- df_bond.close.shift(-1))/df_bond.close.shift(-1)return ret_stock,ret_bond.sort_index()def ret_same_time(x):return x[x.index>=mean(max(t1,t2,t3,t4,t5)).dropna().index[0] ]ret_stock=ret_same_time(ret_base()[0]).sort_index()#ret_base()[0][ret_base()[0].index>=mean(max(t1,t2,t3,t4,t5)).dropna().index[0] ]ret_bond= ret_same_time(ret_base()[1] )#ret_base()[1][ret_base()[1].index>=mean(max(t1,t2,t3,t4,t5)).dropna().index[0] ]DF=ret_same_time(df_stock.close).sort_index()mean1=ret_same_time(mean(t1))mean2=ret_same_time(mean(t2))mean3=ret_same_time(mean(t3))mean4=ret_same_time(mean(t4))mean5=ret_same_time(mean(t5))def sig_fun():sig_stock=pd.Series(0,ret_stock.index )sig_bond= pd.Series(0,ret_bond.sort_index().index)for i in range(math.ceil(len(ret_stock)/freq)-1):if DF[i*freq]>n*mean1[i*freq] and DF[i*freq]>n*mean2[i*freq] and DF[i*freq]>n*mean3[i*freq] and DF[i*freq]>n*mean4[i*freq] and DF[i*freq]>n*mean5[i*freq]:for j in range(i*freq,(1+i)*freq):sig_stock[j]=1sig_bond[j]=0else:for j in range(i*freq,(i+1)*freq):sig_stock[j]=0sig_bond[j]=1for i in range(freq*(math.ceil(len(ret_stock)/freq)-1),len(ret_bond)):k=freq*(math.ceil(len(ret_stock)/freq)-1)if DF[k]>mean1[k] and DF[k]>mean2[k] and DF[k]>mean3[k] and DF[k]>mean4[k] and DF[k]>mean5[k]:sig_stock[i]=1sig_bond[i]=0else:sig_stock[i]=0sig_bond[i]=1return sig_stock,sig_bondsig_stock,sig_bond=sig_fun()ret=(ret_stock*sig_stock+ret_bond*sig_bond) .sort_index()#cum=np.cumprod(1+ret.tail(len(ret)-1))def ret_port( ret_bond,ret_stock):ret=ret_bond*sig_bond+ret_stock*sig_stockret=ret.sort_index().dropna()ret_stock=ret_stock.sort_index()ret_bond =ret_bond.sort_index()cum_bond=np.cumprod(1+ret_bond)cum_stock=np.cumprod(1+ret_stock)cum=np.cumprod(1+ret)return cum,cum_stock,cum_bond,retcum,cum_stock,cum_bond,ret=ret_port( ret_bond,ret_stock)#画图def plot():plt.plot(cum_bond ,label="000012.SH",color='k',linestyle='-')plt.plot(cum_stock,label="000300.SH",color='b',linestyle='-')plt.plot(cum,label=" 组合策略（freq=65,[50,70,90,110,130]） ",color='darkred',linestyle='-') plt.title("净值走势")plt.legend(loc="upper left")#结果描述统计 def performance(port_ret):port_ret=port_ret.sort_index(ascending=True)first_date = port_ret.index[0]final_date = port_ret.index[-1]time_interval = (final_date - first_date).days * 250 / 365# calculate portfolio's indicatornv = (1 + port_ret).cumprod()arith_mean = port_ret.mean() * 250geom_mean = (1 + port_ret).prod() ** (250 / time_interval) - 1sd = port_ret.std() * np.sqrt(250)mdd = ((nv.cummax() - nv) / nv.cummax()).max()sharpe = (geom_mean - 0) / sdcalmar = geom_mean / mddresult = pd.DataFrame({'算术平均收益': [arith_mean], '几何平均收益': [geom_mean], '波动率': [sd],'最大回撤率': [mdd], '夏普比率': [sharpe], '卡尔曼比率': [calmar]})print (result)return plot(),performance(ret)if __name__=="__main__":momentum(freq=65,test_start="1201",test_end="2001",t1=50,t2=70,t3=90,t4=110,t5=130,n=1)

算术平均收益几何平均收益波动率最大回撤率夏普比率卡尔曼比率0.153452 0.144932 0.1687160.4333930.85903 0.334412