#!/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#正常顯示畫圖時出現的中文和負號
from pylab import mpl
mpl.rcParams['font.sans-serif']=['SimHei']
mpl.rcParams['axes.unicode_minus']=False#引入TA-Lib庫
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('上證綜指走勢',fontsize=15)
plt.subplot(212)
df['mfi'].plot()
plt.title('MFI指標',fontsize=15)
plt.xlabel('')
plt.show()# 計算方法  
# 
# 1.典型價格（TP）=當日最高價、最低價與收盤價的算術平均值  
# 
# 2.貨幣流量（MF）=典型價格（TP）×N日內成交量  
# 
# 3.如果當日MF>昨日MF，則將當日的MF值視為正貨幣流量（PMF）  
# 
# 4.如果當日MF<昨日MF，則將當日的MF值視為負貨幣流量（NMF）  
#  
# 5.MFI=100-[100/(1+PMF/NMF)]  
# 
# 6.參數N一般設為14日。  
# 
# 應用法則  
# 
# 1.顯示超買超賣是MFI指標最基本的功能。當MFI>80時為超買，在其回頭向下跌破80時，為短線賣出時機。
# 
# 2.當MFI<20時為超賣，當其回頭向上突破20時，為短線買進時機。
# 
# 3.當MFI>80，而產生背離現象時，視為賣出信號。
# 
# 4.當MFI<20，而產生背離現象時，視為買進信號。
# 
# 注意要點
# 
# 1.經過長期測試，MFI指標的背離訊號更能忠實的反應股價的反轉現象。一次完整的波段行情，至少都會維持一定相當的時間，反轉點出現的次數并不會太多。
# 
# 2.將MFI指標的參數設定為14天時，其背離訊號產生的時機，大致上都能和股價的頂點吻合。因此在使用MFI指標時，參數設定方面應盡量維持14日的原則。
# 熔融流動指數:MFI,無紡布熔融噴絲中常用參數。# In[186]:#當前日的MFI<20，而當日的MFI>20時，買入信號設置為1
for i in range(15,len(df)):if df['mfi'][i]>20 and df['mfi'][i-1]<20:df.loc[df.index[i],'收盤信號']=1if df['mfi'][i]<80 and df['mfi'][i-1]>80:df.loc[df.index[i],'收盤信號']=0#計算每天的倉位，當天持有上證指數時，倉位為1，當天不持有上證指數時，倉位為0
pd.options.mode.chained_assignment = None
df['當天倉位']=df['收盤信號'].shift(1)
df['當天倉位'].fillna(method='ffill',inplace=True)from datetime import datetime,timedelta
d=df[df['當天倉位']==1].index[0]-timedelta(days=1)
df_new=df.loc[d:]
df_new['ret'][0]=0
df_new['當天倉位'][0]=0#當倉位為1時，買入上證指數，當倉位為0時，空倉，計算資金指數
df_new['資金指數']=(df_new.ret*df['當天倉位']+1.0).cumprod()
df_new['指數凈值']=(df_new.ret+1.0).cumprod()df.close.plot(figsize=(16,7))
for i in range(len(df)):if df['收盤信號'][i]==1:plt.annotate('買',xy=(df.index[i],df.close[i]),arrowprops=dict(facecolor='r',shrink=0.05))if df['收盤信號'][i]==0:plt.annotate('賣',xy=(df.index[i],df.close[i]),arrowprops=dict(facecolor='g',shrink=0.1))    
plt.title('上證指數2000-2019年MFI買賣信號',size=15)
plt.xlabel('')
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()#查看最近兩年情況
df1=df.loc['2016-01-01':,]
df1.close.plot(figsize=(16,7))
for i in range(len(df1)):if df1['收盤信號'][i]==1:plt.annotate('買',xy=(df1.index[i],df1.close[i]),arrowprops=dict(facecolor='r',shrink=0.05))if df1['收盤信號'][i]==0:plt.annotate('賣',xy=(df1.index[i],df1.close[i]),arrowprops=dict(facecolor='g',shrink=0.1))    
plt.title('上證指數2016-2019年MFI買賣信號',fontsize=15)
plt.xlabel('')
ax=plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.show()df1['策略凈值']=(df1.ret*df1['當天倉位']+1.0).cumprod()
df1['指數凈值']=(df1.ret+1.0).cumprod()df1['策略收益率']=df1['策略凈值']/df1['策略凈值'].shift(1)-1
df1['指數收益率']=df1.ret
total_ret=df1[['策略凈值','指數凈值']].iloc[-1]-1
annual_ret=pow(1+total_ret,250/len(df_new))-1
dd=(df1[['策略凈值','指數凈值']].cummax()-df1[['策略凈值','指數凈值']])/df1[['策略凈值','指數凈值']].cummax()
d=dd.max()
beta=df1[['策略收益率','指數收益率']].cov().iat[0,1]/df1['指數收益率'].var()
alpha=(annual_ret['策略凈值']-annual_ret['指數凈值']*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['策略凈值']*100,2)
TA2=round(total_ret['指數凈值']*100,2)
AR1=round(annual_ret['策略凈值']*100,2)
AR2=round(annual_ret['指數凈值']*100,2)
MD1=round(d['策略凈值']*100,2)
MD2=round(d['指數凈值']*100,2)
S=round(sharper_atio,2)
print(f'累計收益率：策略{TA1}%，指數{TA2}%;\n年化收益率：策略{AR1}%，指數{AR2}%；\n最大回撤：  策略{MD1}%，指數{MD2}%;\n策略alpha: {round(alpha,2)}，策略beta：{round(beta,2)}; \n夏普比率：  {S}')df1[['策略凈值','指數凈值']].plot(figsize=(15,7))
plt.title('上證指數與MFI指標策略\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'累計收益率：策略{TA1}%，指數{TA2}%;\n年化收益率：策略{AR1}%，指數{AR2}%；\n最大回撤：  策略{MD1}%，指數{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#關掉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],'收盤信號']=1if df['mfi'][i]<H and df['mfi'][i-1]>H:df.loc[df.index[i],'收盤信號']=0df['當天倉位']=df['收盤信號'].shift(1)df['當天倉位'].fillna(method='ffill',inplace=True)d=df[df['當天倉位']==1].index[0]-timedelta(days=1)df1=df.loc[d:]df1['ret'][0]=0df1['當天倉位'][0]=0#當倉位為1時，買入上證指數，當倉位為0時，空倉，計算資金指數df1['策略凈值']=(df1.ret.values*df1['當天倉位'].values+1.0).cumprod()df1['指數凈值']=(df1.ret.values+1.0).cumprod()df1['策略收益率']=df1['策略凈值']/df1['策略凈值'].shift(1)-1df1['指數收益率']=df1.rettotal_ret=df1[['策略凈值','指數凈值']].iloc[-1]-1annual_ret=pow(1+total_ret,250/len(df_new))-1dd=(df1[['策略凈值','指數凈值']].cummax()-df1[['策略凈值','指數凈值']])/df1[['策略凈值','指數凈值']].cummax()d=dd.max()beta=df1[['策略收益率','指數收益率']].cov().iat[0,1]/df1['指數收益率'].var()alpha=(annual_ret['策略凈值']-annual_ret['指數凈值']*beta)exReturn=df1['策略收益率']-0.03/250sharper_atio=np.sqrt(len(exReturn))*exReturn.mean()/exReturn.std()TA1=round(total_ret['策略凈值']*100,2)TA2=round(total_ret['指數凈值']*100,2)AR1=round(annual_ret['策略凈值']*100,2)AR2=round(annual_ret['指數凈值']*100,2)MD1=round(d['策略凈值']*100,2)MD2=round(d['指數凈值']*100,2)S=round(sharper_atio,2)df1[['策略凈值','指數凈值']].plot(figsize=(15,7))plt.title('上證指數與MFI指標策略\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['指數凈值'].max()/1.5, f'累計收益率：策略{TA1}%，指數{TA2}%;\n年化收益率：策略{AR1}%，指數{AR2}%；\n最大回撤：  策略{MD1}%，指數{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)