回測一波
這次小研究可能要探討，中短期的技術指標，我想假設15天到30天採用不同的技術主標結算後，假設報酬率，不是最高的則，退回尋找最高回傳的報酬率，這段時間並輸出這段時間的，DATA可以詳細分析這段時間到底出了什麼事資料量越多或許可以結合ai作權重的提升，讓程式在適當的時機做出勝率最高的判斷，我想ai大概是這樣吧?!，這是下一次的目標 
改得也不錯吧!?，樓下xq操盤高手 
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 20 17:46:15 2017
@author: user
"""
import numpy as np
import pandas as pd
import csv
import talib
import os
import time
import matplotlib.pyplot as plt
from mpl_finance import candlestick_ohlc
import matplotlib.dates as dates
from datetime import datetime
data_list = []
num_48 = ["1216統一"]
df=pd.DataFrame()
df2=pd.DataFrame(num_48)
for iii in num_48:
str=os.path.join("C:\\","Users","x2132","OneDrive","桌面","History_data",iii)
str=str+'.csv'
print (str)
#收盤
with open(str,'r') as c:
reader=csv.DictReader(c)
c=[row["收盤"] for row in reader] #要導入的列
#開盤
with open(str,'r') as o:
reader=csv.DictReader(o)
o=[row["開盤"] for row in reader] #要導入的列
#最高
with open(str,'r') as h:
reader=csv.DictReader(h)
h=[row["最高"] for row in reader] #要導入的列
#最低
with open(str,'r') as l:
reader=csv.DictReader(l)
l=[row["最低"] for row in reader] #要導入的列
#日期
with open(str,'r') as d:
reader=csv.DictReader(d)
d=[row["日期"] for row in reader] #要導入的列
ope = [float(x) for x in o]
close = [float(x) for x in c]
high = [float(x) for x in h]
low = [float(x) for x in l]
date =d
for x in range(0,len(ope),1):
#datess =dates.date2num(d[x])
#print (datetime.datetime(2017, 3, 13))
#print (dates.date2num(datetime.datetime(2017, 3, 13, 12, 0)))
#print (datetime.datetime(2017, 3, 13, 12, 0))
t = d[x].replace('/','-')
#print ((dates.date2num(datetime.strptime(t, '%Y-%m-%d'))))
# (type(datetime.datetime(2017, 3, 13, 12, 0)))
#print (type(dates.date2num(datetime.datetime(2017, 3, 13, 12, 0))))
#print(dates.date2num(datetime.datetime(2017, 3, 13, 12, 0)))
datas = (dates.date2num(datetime.strptime(t, '%Y-%m-%d')) , ope[x], high[x], low[x],close[x])
data_list.append(datas)
date.reverse()
low.reverse()
high.reverse()
close.reverse()
ope.reverse()
#技術指標
def get_MACD():
MACD = talib.MACD(np.array(close),
fastperiod=12, slowperiod=26, signalperiod=9)
return [float(x) for x in MACD [1]]
def get_DIF(test):
EMA12=talib.EMA(np.array(close), timeperiod=12)
EMA26=talib.EMA(np.array(close), timeperiod=26)
for tmp in range(0,len(EMA12)):
test.append(float(EMA12[tmp]-EMA26[tmp]))
DIF=[]
TRUE_MACD=get_MACD()
get_DIF(DIF)
#交易策略
win=[]
win_con=[0]
los_con=[0]
buy_data=[]
self_data=[]
buy_date=[]
self_date=[]
star=10000000 #一開始的錢
price=10000000
handle=0 #持有股數
ex_handle=1 #持有股數上限
re_handle=0 #實際持有
for tmp in range(12,len(DIF)):
if(tmp+1<len(DIF)):
if(DIF[tmp]>TRUE_MACD[tmp] and price >= (ex_handle*close[tmp]*1000) ):
handle= handle+ex_handle*1000
re_handle=re_handle+ex_handle
fin_price=price-(ex_handle*ope[tmp+1])*1000 #1=1000
print ('買進日期',date[tmp+1],'開盤價',ope[tmp+1],'支出',(ex_handle*ope[tmp+1])*1000,'結算金額',fin_price,'持有股數',handle,'持有張數',re_handle)
str='買進日期',date[tmp+1],'開盤價',ope[tmp+1],'支出',(ex_handle*ope[tmp+1])*1000,'結算金額',fin_price,'持有股數',handle,'持有張數',re_handle
buy_data.append(str)
elif (DIF[tmp]<TRUE_MACD[tmp] and re_handle >=1 ):
fin_price=price+(re_handle*close[tmp])*1000
handle=0
re_handle=0
print ('賣出日期',date[tmp],'收盤價',close[tmp],'結算金額',fin_price,'持有股數',handle,'持有張數',re_handle)
str='賣出日期',date[tmp],'收盤價',close[tmp],'結算金額',fin_price,'持有股數',handle,'持有張數',re_handle
self_data.append(str)
if (handle>0):#強制平倉
fin_price+= (close[len(close)-1]*handle)
handle=0
re_handle=0
print ('強制平倉 賣出日期',date[len(close)-1],'收盤價',close[len(close)-1],'結算金額',fin_price,'持有股數',handle,'持有張數',re_handle)
#回測績效 - 交易次數
count=0
loss=0
for a,b in zip (buy_data[:],self_data[:]):
buy_date.append(a[3])
self_date.append(b[3])
if buy_date[0]<self_date[0]:
count+=1
else:
loss+=1
del buy_date[0]
del self_date[0]
for a,b in zip (buy_data[:],self_data[:]):
buy_date.append(a[3])
self_date.append(b[3])
if len(buy_date)>0:
z=(self_date[0]-buy_date[0])/buy_date[0]
if z>0:
win_con.append(z)
elif z<0:
los_con.append(z)
del buy_date[0]
del self_date[0]
for tmp in buy_data:
print (tmp)
for tmp in self_data:
print (tmp)
wp=int(fin_price-star)
buy=len(buy_data)
sel=len(self_data)
total_trade=sum((len(buy_data),len(self_data)))
win_rate=round(count/sum((len(buy_data),len(self_data)))*100,2)
retur=round(((fin_price-star)/star)*100,2)
ave_retur=round((((fin_price-star)/star))/(sum((len(buy_data),len(self_data))))*100,4)
win_num=count
los_num=loss
max_win=round(max(win_con),2)
min_los=round(min(los_con),2)
data={"獲利金額":[wp],"買次數":[buy],"賣次數":[sel],"總交易次數":[total_trade],"勝率":[win_rate],"報酬率":[retur],"平均報酬率":[ave_retur],"獲利次數":[win_num],"虧損次數":[los_num],"最大獲利率":[max_win],"最大虧損率":[min_los]}
df=df.append(pd.DataFrame(data),ignore_index=True)
df.insert(0,'股票代號',df2)
df.to_csv("MACD.KPI.csv")
print ('獲利金額:',(int(fin_price-star)))
print ('交易次數 買/賣:',len(buy_data),'/',len(self_data))
print ('總交易次數:',sum((len(buy_data),len(self_data))))
print ('勝率:%5.2f'%(count/sum((len(buy_data),len(self_data)))*100),"%")
print ('報酬率:',round(((fin_price-star)/star)*100,2))
print ('報酬率:%5.2f'%(((fin_price-star)/star)*100),"%")
print ('平均報酬率',round((((fin_price-star)/star))/(sum((len(buy_data),len(self_data))))*100,4),"%")
print("獲勝次數:",count)
print("虧損次數:",loss)
print("最大獲利率:",round(max(win_con),2),"%")
print("最大虧損率:",round(min(los_con),2),"%")
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
# 设置X轴刻度为日期时间
ax.xaxis_date()
plt.xticks(rotation=45)
plt.yticks()
plt.title("k線圖")
plt.xlabel("時間")
plt.ylabel("股價")
print (len(data_list))
candlestick_ohlc(ax, data_list[:], width=0.6, colorup='r', colordown='g')
print('hello')
plt.grid()
plt.show()
print (data_list)
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