#!/usr/bin/env python
# coding: utf-8#先引入后面可能用到的包（package）
import pandas as pd  
import numpy as np
import matplotlib.pyplot as plt#正常显示画图时出现的中文和负号
from pylab import mpl
mpl.rcParams['font.sans-serif']=['SimHei']
mpl.rcParams['axes.unicode_minus']=False#引入TA-Lib库
import talib as ta
import time
from datetime import datetime, timedelta
# import tushare as ts
# df=ts.get_k_data('sh',start='2000-01-01')
# df.index=pd.to_datetime(df.date)
# df=df.sort_index()
# df['ret']=df.close/df.close.shift(1)-1
# df.head()
import akshare as ak
from_date = '2010-01-01'
from_date = datetime.strptime(from_date,"%Y-%m-%d")
day_nums = 1
current_dt = time.strftime("%Y-%m-%d", time.localtime())
current_dt = datetime.strptime(current_dt, '%Y-%m-%d')
df = ak.stock_zh_a_daily(symbol='sh000001',start_date = from_date,end_date =  current_dt)
df.index=pd.to_datetime(df.date)
df=df.sort_index()
df['ret']=df.close/df.close.shift(1)-1high,low,close,volume=df.high.values,df.low.values,df.close.values,df.volume.values
df['mfi']=ta.MFI(high, low, close, volume, timeperiod=14)
plt.figure(figsize=(16,14))
plt.subplot(211)
df['close'].plot(color='r')
plt.xlabel('')
plt.title('上证综指走势',fontsize=15)
plt.subplot(212)
df['mfi'].plot()
plt.title('MFI指标',fontsize=15)
plt.xlabel('')
plt.show()# 计算方法  
# 
# 1.典型价格（TP）=当日最高价、最低价与收盘价的算术平均值  
# 
# 2.货币流量（MF）=典型价格（TP）×N日内成交量  
# 
# 3.如果当日MF>昨日MF，则将当日的MF值视为正货币流量（PMF）  
# 
# 4.如果当日MF<昨日MF，则将当日的MF值视为负货币流量（NMF）  
#  
# 5.MFI=100-[100/(1+PMF/NMF)]  
# 
# 6.参数N一般设为14日。  
# 
# 应用法则  
# 
# 1.显示超买超卖是MFI指标最基本的功能。当MFI>80时为超买，在其回头向下跌破80时，为短线卖出时机。
# 
# 2.当MFI<20时为超卖，当其回头向上突破20时，为短线买进时机。
# 
# 3.当MFI>80，而产生背离现象时，视为卖出信号。
# 
# 4.当MFI<20，而产生背离现象时，视为买进信号。
# 
# 注意要点
# 
# 1.经过长期测试，MFI指标的背离讯号更能忠实的反应股价的反转现象。一次完整的波段行情，至少都会维持一定相当的时间，反转点出现的次数并不会太多。
# 
# 2.将MFI指标的参数设定为14天时，其背离讯号产生的时机，大致上都能和股价的顶点吻合。因此在使用MFI指标时，参数设定方面应尽量维持14日的原则。
# 熔融流动指数:MFI,无纺布熔融喷丝中常用参数。# In[186]:#当前日的MFI<20，而当日的MFI>20时，买入信号设置为1
for i in range(15,len(df)):if df['mfi'][i]>20 and df['mfi'][i-1]<20:df.loc[df.index[i],'收盘信号']=1if df['mfi'][i]<80 and df['mfi'][i-1]>80:df.loc[df.index[i],'收盘信号']=0#计算每天的仓位，当天持有上证指数时，仓位为1，当天不持有上证指数时，仓位为0
pd.options.mode.chained_assignment = None
df['当天仓位']=df['收盘信号'].shift(1)
df['当天仓位'].fillna(method='ffill',inplace=True)from datetime import datetime,timedelta
d=df[df['当天仓位']==1].index[0]-timedelta(days=1)
df_new=df.loc[d:]
df_new['ret'][0]=0
df_new['当天仓位'][0]=0#当仓位为1时，买入上证指数，当仓位为0时，空仓，计算资金指数
df_new['资金指数']=(df_new.ret*df['当天仓位']+1.0).cumprod()
df_new['指数净值']=(df_new.ret+1.0).cumprod()df.close.plot(figsize=(16,7))
for i in range(len(df)):if df['收盘信号'][i]==1:plt.annotate('买',xy=(df.index[i],df.close[i]),arrowprops=dict(facecolor='r',shrink=0.05))if df['收盘信号'][i]==0:plt.annotate('卖',xy=(df.index[i],df.close[i]),arrowprops=dict(facecolor='g',shrink=0.1))    
plt.title('上证指数2000-2019年MFI买卖信号',size=15)
plt.xlabel('')
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()#查看最近两年情况
df1=df.loc['2016-01-01':,]
df1.close.plot(figsize=(16,7))
for i in range(len(df1)):if df1['收盘信号'][i]==1:plt.annotate('买',xy=(df1.index[i],df1.close[i]),arrowprops=dict(facecolor='r',shrink=0.05))if df1['收盘信号'][i]==0:plt.annotate('卖',xy=(df1.index[i],df1.close[i]),arrowprops=dict(facecolor='g',shrink=0.1))    
plt.title('上证指数2016-2019年MFI买卖信号',fontsize=15)
plt.xlabel('')
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()df1['策略净值']=(df1.ret*df1['当天仓位']+1.0).cumprod()
df1['指数净值']=(df1.ret+1.0).cumprod()df1['策略收益率']=df1['策略净值']/df1['策略净值'].shift(1)-1
df1['指数收益率']=df1.ret
total_ret=df1[['策略净值','指数净值']].iloc[-1]-1
annual_ret=pow(1+total_ret,250/len(df_new))-1
dd=(df1[['策略净值','指数净值']].cummax()-df1[['策略净值','指数净值']])/df1[['策略净值','指数净值']].cummax()
d=dd.max()
beta=df1[['策略收益率','指数收益率']].cov().iat[0,1]/df1['指数收益率'].var()
alpha=(annual_ret['策略净值']-annual_ret['指数净值']*beta)
exReturn=df1['策略收益率']-0.03/250
sharper_atio=np.sqrt(len(exReturn))*exReturn.mean()/exReturn.std()
TA1=round(total_ret['策略净值']*100,2)
TA2=round(total_ret['指数净值']*100,2)
AR1=round(annual_ret['策略净值']*100,2)
AR2=round(annual_ret['指数净值']*100,2)
MD1=round(d['策略净值']*100,2)
MD2=round(d['指数净值']*100,2)
S=round(sharper_atio,2)
print(f'累计收益率：策略{TA1}%，指数{TA2}%;\n年化收益率：策略{AR1}%，指数{AR2}%；\n最大回撤：  策略{MD1}%，指数{MD2}%;\n策略alpha: {round(alpha,2)}，策略beta：{round(beta,2)}; \n夏普比率：  {S}')df1[['策略净值','指数净值']].plot(figsize=(15,7))
plt.title('上证指数与MFI指标策略\n2016年1月1日至今',size=15)bbox = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)plt.text('2017-05-01', 0.75, f'累计收益率：策略{TA1}%，指数{TA2}%;\n年化收益率：策略{AR1}%，指数{AR2}%；\n最大回撤：  策略{MD1}%，指数{MD2}%;\n策略alpha: {round(alpha,2)}，策略beta：{round(beta,2)}; \n夏普比率：  {S}',         size=13,bbox=bbox)  
plt.xlabel('')
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()def get_data(code,date):#df=ts.get_k_data(code,start=date)from_date = '2010-01-01'from_date = datetime.strptime(from_date,"%Y-%m-%d")day_nums = 1current_dt = time.strftime("%Y-%m-%d", time.localtime())current_dt = datetime.strptime(current_dt, '%Y-%m-%d')df = ak.stock_zh_a_daily(symbol='sh000001',start_date = from_date,end_date =  current_dt)df.index=pd.to_datetime(df.date)df=df.sort_index()df['ret']=df.close/df.close.shift(1)-1return df#关掉pandas的warnings
pd.options.mode.chained_assignment = None
def strategy(code,date,L,H):df=get_data(code,date)high,low,close,volume=df.high.values,df.low.values,df.close.values,df.volume.valuesdf['mfi']=ta.MFI(high, low, close, volume, timeperiod=14)for i in range(14,len(df)):if df['mfi'][i]>L and df['mfi'][i-1]<L:df.loc[df.index[i],'收盘信号']=1if df['mfi'][i]<H and df['mfi'][i-1]>H:df.loc[df.index[i],'收盘信号']=0df['当天仓位']=df['收盘信号'].shift(1)df['当天仓位'].fillna(method='ffill',inplace=True)d=df[df['当天仓位']==1].index[0]-timedelta(days=1)df1=df.loc[d:]df1['ret'][0]=0df1['当天仓位'][0]=0#当仓位为1时，买入上证指数，当仓位为0时，空仓，计算资金指数df1['策略净值']=(df1.ret.values*df1['当天仓位'].values+1.0).cumprod()df1['指数净值']=(df1.ret.values+1.0).cumprod()df1['策略收益率']=df1['策略净值']/df1['策略净值'].shift(1)-1df1['指数收益率']=df1.rettotal_ret=df1[['策略净值','指数净值']].iloc[-1]-1annual_ret=pow(1+total_ret,250/len(df_new))-1dd=(df1[['策略净值','指数净值']].cummax()-df1[['策略净值','指数净值']])/df1[['策略净值','指数净值']].cummax()d=dd.max()beta=df1[['策略收益率','指数收益率']].cov().iat[0,1]/df1['指数收益率'].var()alpha=(annual_ret['策略净值']-annual_ret['指数净值']*beta)exReturn=df1['策略收益率']-0.03/250sharper_atio=np.sqrt(len(exReturn))*exReturn.mean()/exReturn.std()TA1=round(total_ret['策略净值']*100,2)TA2=round(total_ret['指数净值']*100,2)AR1=round(annual_ret['策略净值']*100,2)AR2=round(annual_ret['指数净值']*100,2)MD1=round(d['策略净值']*100,2)MD2=round(d['指数净值']*100,2)S=round(sharper_atio,2)df1[['策略净值','指数净值']].plot(figsize=(15,7))plt.title('上证指数与MFI指标策略\n'+date+'至今',size=15)bbox = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)plt.text(df1.index[int(len(df1)/5)], df1['指数净值'].max()/1.5, f'累计收益率：策略{TA1}%，指数{TA2}%;\n年化收益率：策略{AR1}%，指数{AR2}%；\n最大回撤：  策略{MD1}%，指数{MD2}%;\n策略alpha: {round(alpha,2)}，策略beta：{round(beta,2)}; \n夏普比率：  {S}',size=13,bbox=bbox)  plt.xlabel('')ax=plt.gca()ax.spines['right'].set_color('none')ax.spines['top'].set_color('none')plt.show()strategy('sh','2009-05-12',20,80)strategy('sh','2009-04-12',20,90)strategy('sh','2009-04-12',20,95)strategy('sh','2009-04-12',30,95)strategy('sh','2009-04-12',15,95)strategy('sh','2016-01-01',20,90)strategy('sh','2000-01-01',20,80)strategy('sh','2000-01-01',20,92)strategy('sh','2017-04-12',20,80)strategy('sh','2017-04-12',20,92)strategy('cyb','2017-04-01',20,80)