#导入需要的库
import pandas as pd
import numpy as np
from jqdata import jy
from jqdata import *
import seaborn as sns
import matplotlib as mpl
from cvxopt import solvers, matrix
import datetime as dt
from scipy.optimize import minimize

#获取所需的数据
s_date,e_date = '2012-11-10','2018-12-10'
moneyfund_price = get_price('511880.XSHG',start_date=s_date,end_date=e_date)
#通过聚源数据获取申万行业指数行情数据（输入行业指数代码）
def get_SW_index(SW_index = 801010,start_date = '2017-01-31',end_date = '2018-01-31'):
    index_list = ['PrevClosePrice','OpenPrice','HighPrice','LowPrice','ClosePrice','TurnoverVolume','TurnoverValue','TurnoverDeals','ChangePCT','UpdateTime']
    jydf = jy.run_query(query(jy.SecuMain).filter(jy.SecuMain.SecuCode==str(SW_index)))
    link=jydf[jydf.SecuCode==str(SW_index)]
    rows=jydf[jydf.SecuCode==str(SW_index)].index.tolist()
    result=link['InnerCode'][rows]

    df = jy.run_query(query(jy.QT_SYWGIndexQuote).filter(jy.QT_SYWGIndexQuote.InnerCode==str(result[0]),                                                   jy.QT_SYWGIndexQuote.TradingDay>=start_date,                                                         jy.QT_SYWGIndexQuote.TradingDay<=end_date
                                                        ))
    df.index = df['TradingDay']
    df = df[index_list]
    return df

hy_df = get_industries(name='sw_l1')

hy_price_dict = {}
for hy in hy_df.index[:]:
    hy_price =get_SW_index(SW_index = hy,start_date = s_date,end_date = e_date)
    hy_price_dict[hy] = hy_price['ClosePrice']
all_price_df = pd.concat(hy_price_dict.values(),axis=1)
all_price_df.columns = hy_df.index
#all_price_df['moneyfund'] = moneyfund_price['close']
all_price_df.head(5)

mpl.pyplot.style.use('ggplot')
all_price_df.columns = list(hy_df['name'].values)#+['货币基金']
mpl.rcParams['font.family']='serif'
mpl.rcParams['axes.unicode_minus']=False # 处理负号
prices=all_price_df
(prices/prices.iloc[0]).plot(figsize=(18,12),grid='on')
pct_daily = prices.pct_change()

#计算各行业指数相关矩阵
fig = plt.figure(figsize= (15,10))
ax = fig.add_subplot(111)
ax = sns.heatmap(pct_daily.corr(),annot=True,annot_kws={'size':9,'weight':'bold'})

#获取日期列表
#按月、季度、半年方式获取交易日列表
def get_tradeday_list(start,end,frequency=None,count=None):
    if count != None:
        df = get_price('000001.XSHG',end_date=end,count=count)
    else:
        df = get_price('000001.XSHG',start_date=start,end_date=end)
    if frequency == None or frequency =='day':
        return df.index
    else:
        df['year-month'] = [str(i)[0:7] for i in df.index]
        if frequency == 'month':
            return df.drop_duplicates('year-month').index
        elif frequency == 'quarter':
            df['month'] = [str(i)[5:7] for i in df.index]
            df = df[(df['month']=='01') | (df['month']=='04') | (df['month']=='07') | (df['month']=='10') ]
            return df.drop_duplicates('year-month').index
        elif frequency =='halfyear':
            df['month'] = [str(i)[5:7] for i in df.index]
            df = df[(df['month']=='01') | (df['month']=='06')]
            return df.drop_duplicates('year-month').index 

get_tradeday_list(start=s_date,end=e_date,frequency="quarter",count=None)

s_date,e_date = '2013-12-10','2018-12-10'
tradedays = get_tradeday_list(start=s_date,end=e_date,frequency="quarter",count=None)
tradedays

#计算调仓时权重配置并保存为字典
#记录加入权重后的组合收益时间序列
t0=dt.datetime.now()
weights_dict = {}
returns_df = pd.DataFrame()
num = 1
ret_daily = pct_daily+1
weights_name = ['mean_weights','mean_vol','min_var','risk_parity','max_sharpe']
for date_temp1,date_temp2 in zip(tradedays[:-1],tradedays[1:]):
    t1=dt.datetime.now()
    #获取指定日期一年前的交易日日期
    def get_before_tradeday(date):
        return get_price('000001.XSHG',end_date=date,count=252).index[0]
    date_before = get_before_tradeday(date_temp1)
    #最小方差
    def fun1(x):
        risk =  np.dot(x.T,np.dot(cov_mat,x))
        return risk
    #最大夏普
    def fun2(x):
        r_b = 0.04
        pct_mean = pct_temp.mean()
        p_r = (1+np.dot(x,pct_mean))**250-1
        p_sigma = np.sqrt(np.dot(x0.T,np.dot(cov_mat,x0))*250)
        p_sharpe = (p_r-r_b)/p_sigma
        return -p_sharpe
    #风险平价
    def fun3(x):
        tmp = (omega * np.matrix(x).T).A1
        risk = x * tmp/ np.sqrt(np.matrix(x) * omega * np.matrix(x).T).A1[0]
        delta_risk = [sum((i - risk)**2) for i in risk]
        return sum(delta_risk)
    #计算协方差矩阵
    pct_temp = pct_daily.loc[date_before:date_temp1,:]
    cov_mat = pct_temp.cov()
    omega = np.matrix(cov_mat.values)
    #sicpy优化方法参数设置
    x0 = np.ones(omega.shape[0]) / omega.shape[0]  
    bnds = tuple((0,None) for x in x0)
    cons = ({'type':'eq', 'fun': lambda x: sum(x) - 1})
    options={'disp':False, 'maxiter':1000, 'ftol':1e-20}
    
    #平均分配权重
    weights1 = np.array([1.0/pct_temp.shape[1]]*pct_temp.shape[1])
    #等波动率
    wts = 1/pct_temp.std()
    weights2 = wts/wts.sum()
    #模型求解组合方差最小时权重
    weights3 = minimize(fun1,x0,bounds=bnds,constraints=cons,method='SLSQP',options=options)['x']
    #风险平价
    weights4 = minimize(fun3,x0,bounds=bnds,constraints=cons,method='SLSQP',options=options)['x']
    #最大夏普
    weights5 = minimize(fun2,x0,bounds=bnds,constraints=cons,method='SLSQP',options=options)['x']
    weights_list = [weights1,weights2,weights3,weights4,weights5]
    #统计收益
    if num == 0:
        returns1_df = pd.DataFrame()
        for i in range(len(weights_list)):
            weights_temp =[j if j > 0.0001 else 0.0 for j in weights_list[i]]
            returns1_df[weights_name[i]] = (ret_daily.loc[date_temp1:date_temp2,:]*weights_temp).cumsum(axis=1).iloc[:,-1]
        returns_df = pd.concat([returns_df,returns1_df],axis=0)
    else:
        #按权重的方式计算
        for i in range(len(weights_list)):
            weights_temp =[j if j > 0.0001 else 0.0 for j in weights_list[i]]
            returns_df[weights_name[i]] = (ret_daily.loc[date_temp1:date_temp2,:]*weights_temp).cumsum(axis=1).iloc[:,-1]
        num = 0
    weights_dict[date_temp1] = weights_list
    t2=dt.datetime.now()
    print('计算到%s,已耗时%s秒'%(date_temp2,(t2-t1).seconds))
print('计算完毕,总耗时%s秒'%(t2-t0).seconds)

returns_df.cumprod(axis=0).plot(figsize=(15,8))

mean_wts_df = pd.DataFrame()
meanvol_wts_df = pd.DataFrame()
minvar_wts_df = pd.DataFrame()
rp_wts_df = pd.DataFrame()
maxshp_wts_df = pd.DataFrame()
for date in weights_dict.keys():
    #print(weights_dict[date][1])
    mean_wts_df[date]  = weights_dict[date][0]
    meanvol_wts_df[date]  = weights_dict[date][1]
    minvar_wts_df[date]  = weights_dict[date][2]
    rp_wts_df[date]  = weights_dict[date][3]
    maxshp_wts_df[date]  = weights_dict[date][4]

mean_wts_df = mean_wts_df.T
mean_wts_df.columns = list(hy_df['name'].values)
meanvol_wts_df = meanvol_wts_df.T
meanvol_wts_df.columns = list(hy_df['name'].values)
minvar_wts_df = minvar_wts_df.T
minvar_wts_df.columns = list(hy_df['name'].values)
rp_wts_df = rp_wts_df.T
rp_wts_df.columns = list(hy_df['name'].values)
maxshp_wts_df = maxshp_wts_df.T
maxshp_wts_df.columns = list(hy_df['name'].values)

#权重设置示意图
minvar_wts_df.plot(figsize=(15,8),rot=60,kind='area',ylim=(0,1))

#权重设置示意图
maxshp_wts_df.plot(figsize=(15,8),rot=60,kind='area',ylim=(0,1))

#权重设置示意图
rp_wts_df.plot(figsize=(15,8),rot=60,kind='area',ylim=(0,1))

#权重设置示意图
meanvol_wts_df.plot(figsize=(15,8),rot=60,kind='area',ylim=(0,1))

#计算风险指标函数
def get_risk_index(pct_se):
    return_se = pct_se.cumprod()-1
    total_returns = return_se[-1]
    total_an_returns = ((1+total_returns)**(250/len(return_se))-1)
    vol = pct_se.std()*np.sqrt(250)
    sharpe = (total_an_returns-0.04)/(np.std(pct_se)*250**0.5)
    ret = return_se.dropna()
    ret = ret+1
    maxdown_list = []
    for i in range(1,len(ret)):
        low  = min(ret[i:])
        high = max(ret[0:i]) 
        if high>low:
            #print(high,low)
            maxdown_list.append((high-low)/high)
            #print((high-low)/high)
        else:
            maxdown_list.append(0)
    max_drawdown = max(maxdown_list)
    '''
    print('策略收益：%s'%round(total_returns*100,2)+'%')
    print('策略年化收益：%s'%round(total_an_returns*100,2)+'%')
    print('夏普比率：%s'%round(sharpe,2))
    print('最大回撤：%s'%round(max_drawdown*100,2)+'%')
    print('年化波动率：%s'%round(vol,2))
    '''
    return total_returns,total_an_returns,sharpe,max_drawdown,vol

results=pd.DataFrame(index = ['策略收益','策略年化收益','夏普比率','最大回撤','年化波动率'])
for colu in returns_df.columns:
    results[colu] = get_risk_index(returns_df[colu])
results