Python股票回測
技術指標寫好後，如何測試績效呢?當然是寫個技術指標來跑回測，當然遇上重大事件再配合
機器學習或AI恩???
Stock_backing.py(應該是這樣拼xd
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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Sat Dec 23 17:48:25 2017 | |
| @author: user | |
| """ | |
| import csv | |
| import os | |
| import pandas as pd | |
| import talib | |
| import numpy as np | |
| num_48 = ["1216統一","1301台塑","1303南亞","1326台化","1402遠東新","2002中鋼","2015正新","2207和泰車","2301光寶科","2308台達電","2311日月光","2317鴻海","2324仁寶","2325矽品","2330台積電","2354鴻準","2357華碩","2382廣達","2395研華","2408南亞科","2409友達","2454聯發科","2474可成","2823中壽","2880華南金","2885元大金","2886兆豐金","2891中信金","2892第一金","2912統一超","3045台灣大","3481群創","4938和碩","5880合庫金","6505台塑化"] | |
| df=pd.DataFrame() | |
| df2=pd.DataFrame(num_48) | |
| for iii in num_48: | |
| str=os.path.join("C:\\","Users","YUMI","Desktop","History_data",iii) | |
| str=str+'.csv' | |
| print (str) | |
| with open(str,'r') as o: | |
| reader=csv.DictReader(o) | |
| o=[row["開盤"] for row in reader] #要導入的列 | |
| with open(str,'r') as c: | |
| reader=csv.DictReader(c) | |
| c=[row["收盤"] for row in reader] #要導入的列 | |
| with open(str,'r') as h: | |
| reader=csv.DictReader(h) | |
| h=[row["最高"] for row in reader] #要導入的列 | |
| with open(str,'r') as l: | |
| reader=csv.DictReader(l) | |
| l=[row["最低"] for row in reader] #要導入的列 | |
| with open(str,'r') as d: | |
| reader=csv.DictReader(d) | |
| d=[row["日期"] for row in reader] | |
| 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 | |
| #島狀反轉 黑K跳空紅K跳空紅K | |
| SMA = talib.MA(np.array(close), timeperiod=10, matype=0) | |
| #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 | |
| cb=[]#收盤容器，close box | |
| ob=[]#開盤容器，open box | |
| hb=[]#最高價容器，high box | |
| lb=[]#最低價容器，low box | |
| for tmp in range(10,len(ope)): | |
| if(tmp+1<len(ope)): | |
| cb.append(close[tmp]) | |
| ob.append(ope[tmp]) | |
| hb.append(high[tmp]) | |
| lb.append(low[tmp]) | |
| if len(cb)==4 and len(ob)==4 and len(hb)==4 and len(lb)==4: | |
| #print('收盤',cb) | |
| #print('開盤',ob) | |
| #print('最高',hb) | |
| # print('最低',lb) | |
| if cb[1]>cb[2]>cb[3] and ob[1]>ob[2]>ob[3] and cb[3]>ob[2] and cb[2]>ob[1]: | |
| 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) | |
| else: | |
| if(handle >0): | |
| if close[tmp]<SMA[tmp]: | |
| 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) | |
| del ob[0] | |
| del cb[0] | |
| del hb[0] | |
| del lb[0] | |
| 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) | |
| del ope[tmp] | |
| del close[tmp] | |
| #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("紅三兵.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),"%") | |
| # data=[] | |
| # for x in range(0,20): | |
| # for y in range (x,x+20): | |
| # data.append(buy(y)) | |
| # print(data) | |
| #print ('最大獲利率',max()) | |
| #回測績效 - ROI | |
| #roi = 0 | |
| #winrate = 0 | |
| #def roical(star, price, winrate): | |
| # roi = ((star - price) / price) | |
| # if roi > 0: | |
| # winrate+=1 | |
| # return roi, winrate | |
| #ROI,win=roical(star, price, winrate) | |
| #print (round((ROI*10),2)) | |
| #print (round((win),2)) | |
| ## | |
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