python實(shí)現(xiàn)馬丁策略的實(shí)例詳解

更新時(shí)間：2021年01月15日 15:22:15 作者：達(dá)科索斯

這篇文章主要介紹了python實(shí)現(xiàn)馬丁策略的實(shí)例詳解,本文給大家介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或工作具有一定的參考借鑒價(jià)值，需要的朋友可以參考下

馬丁策略本來(lái)是一種賭博方法，但在投資界應(yīng)用也很廣泛，不過(guò)對(duì)于投資者來(lái)說(shuō)馬丁策略過(guò)于簡(jiǎn)單，所以本文將其改進(jìn)并使得其在震蕩市中獲利，以下說(shuō)明如何實(shí)現(xiàn)馬丁策略。

策略

逢跌加倉(cāng)，間隔由自己決定，每次加倉(cāng)是當(dāng)前倉(cāng)位的一倍。
連續(xù)跌兩次賣出，且賣出一半倉(cāng)位。
如果爆倉(cāng)則全倉(cāng)賣出止損。
初始持倉(cāng)設(shè)置為10%~25%，則可進(jìn)行2到3次補(bǔ)倉(cāng)。

初始化馬丁策略類屬性

def __init__(self,startcash, start, end):
 self.cash = startcash #初始化現(xiàn)金
 self.hold = 0 #初始化持倉(cāng)金額
 self.holdper = self.hold /startcash #初始化倉(cāng)位
 self.log = [] #初始化日志
 self.cost = 0 #成本價(jià) 
 self.stock_num = 0 #股票數(shù)量
 self.starttime = start #起始時(shí)間
 self.endtime = end #終止時(shí)間
 self.quantlog = [] #交易量記錄
 self.earn = [] #總資產(chǎn)記錄
 self.num_log = []
 self.droplog = [0]

為了記錄每次買賣倉(cāng)位的變化初始化了各種列表。

交易函數(shù)

首先導(dǎo)入需要的模塊

import pandas as pd 
import numpy as np
import tushare as ts 
import matplotlib.pyplot as plt

 def buy(self, currentprice, count):

 self.cash -= currentprice*count
 self.log.append('buy')
 self.hold += currentprice*count
 self.holdper = self.hold / (self.cash+ self.hold) 
 self.stock_num += count
 self.cost = self.hold / self.stock_num
 self.quantlog.append(count//100)
 print('買入價(jià)：%.2f，手?jǐn)?shù)：%d，現(xiàn)在成本價(jià)：%.2f，現(xiàn)在持倉(cāng)：%.2f，現(xiàn)在籌碼：%d' %(currentprice ,count//100, self.cost, self.holdper, self.stock_num//100))
 self.earn.append(self.cash+ currentprice*self.stock_num)
 self.num_log.append(self.stock_num)
 self.droplog = [0]
 
 def sell(self, currentprice, count):
 self.cash += currentprice*count
 self.stock_num -= count
 self.log.append('sell')
 self.hold = self.stock_num*self.cost
 self.holdper = self.hold / (self.cash + self.hold)
 #self.cost = self.hold / self.stock_num
 print('賣出價(jià)：%.2f，手?jǐn)?shù)：%d，現(xiàn)在成本價(jià)：%.2f，現(xiàn)在持倉(cāng)：%.2f，現(xiàn)在籌碼：%d' %(currentprice ,count//100, self.cost, self.holdper, self.stock_num//100))
 self.quantlog.append(count//100)    
 self.earn.append(self.cash+ currentprice*self.stock_num)
 self.num_log.append(self.stock_num)
 
 def holdstock(self,currentprice):
 self.log.append('hold')
 #print('持有，現(xiàn)在倉(cāng)位為：%.2f。現(xiàn)在成本：%.2f' %(self.holdper,self.cost))
 self.quantlog.append(0)
 self.earn.append(self.cash+ currentprice*self.stock_num)
 self.num_log.append(self.stock_num)

持倉(cāng)成本的計(jì)算方式是利用總持倉(cāng)金額除以總手?jǐn)?shù)，賣出時(shí)不改變持倉(cāng)成本。持有則是不做任何操作只記錄日志

數(shù)據(jù)接口

def get_stock(self, code):
 df=ts.get_k_data(code,autype='qfq',start= self.starttime ,end= self.endtime)
 df.index=pd.to_datetime(df.date)
 df=df[['open','high','low','close','volume']]
 return df

數(shù)據(jù)接口使用tushare，也可使用pro接口，到官網(wǎng)注冊(cè)領(lǐng)取token。

token = '輸入你的token'
pro = ts.pro_api()
ts.set_token(token)
 def get_stock_pro(self, code):
 code = code + '.SH'
 df = pro.daily(ts_code= code, start_date = self.starttime, end_date= self.endtime)
 return df

數(shù)據(jù)結(jié)構(gòu)：

在這里插入圖片描述

回測(cè)函數(shù)

 def startback(self, data, everyChange, accDropday):
 """
 回測(cè)函數(shù)
 """
 for i in range(len(data)):
  if i < 1:
  continue
  if i < accDropday:
  drop = backtesting.accumulateVar(everyChange, i, i)
  #print('現(xiàn)在累計(jì)漲跌幅度為：%.2f'%(drop))
  self.martin(data[i], data[i-1], drop, everyChange,i)
  elif i < len(data)-2:
  drop = backtesting.accumulateVar(everyChange, i, accDropday)
  #print('現(xiàn)在累計(jì)漲跌幅度為：%.2f'%(drop))
  self.martin(data[i],data[i-1], drop, everyChange,i)
  else:
  if self.stock_num > 0:
   self.sell(data[-1],self.stock_num)
  else: self.holdstock(data[i])

因?yàn)橐?jì)算每日漲跌幅，要計(jì)算差分，所以第一天的數(shù)據(jù)不能計(jì)算在for循環(huán)中跳過(guò)，accDropday是累計(jì)跌幅的最大計(jì)算天數(shù)，用來(lái)控制入場(chǎng)，當(dāng)累計(jì)跌幅大于某個(gè)數(shù)值且倉(cāng)位為0%時(shí)可再次入場(chǎng)。以下是入場(chǎng)函數(shù)：

def enter(self, currentprice,ex_price,accuDrop):
 if accuDrop < -0.01:#and ex_price > currentprice:
  count = (self.cash+self.hold) *0.24 // currentprice //100 * 100
  print('再次入場(chǎng)')
  self.buy(currentprice, count)
 else: self.holdstock(currentprice)

入場(chǎng)倉(cāng)位選擇0.24則可進(jìn)行兩次抄底，如果抄底間隔為7%可承受最大跌幅為14%。

策略函數(shù)

 def martin(self, currentprice, ex_price, accuDrop,everyChange,i):
 diff = (ex_price - currentprice)/ex_price
 self.droplog.append(diff)

 if sum(self.droplog) <= 0:
  self.droplog = [0]
 
 if self.stock_num//100 > 1:
  if sum(self.droplog) >= 0.04:
  if self.holdper*2 < 0.24:
   count =(self.cash+self.hold) *(0.25-self.holdper) // currentprice //100 * 100
   self.buy(currentprice, count)
  elif self.holdper*2 < 1 and (self.hold/currentprice)//100 *100 > 0 and backtesting.computeCon(self.log) < 5:
   self.buy(currentprice, (self.hold/currentprice)//100 *100)
   
  else: self.sell(currentprice, self.stock_num//100 *100);print('及時(shí)止損')

  elif (everyChange[i-2] < 0 and everyChange[i-1] <0 and self.cost < currentprice):# or (everyChange[i-1] < -0.04 and self.cost < currentprice):
   
  if (self.stock_num > 0) and ((self.stock_num*(1/2)//100*100) > 0):
   
   self.sell(currentprice, self.stock_num*(1/2)//100*100 )

   #print("現(xiàn)在累計(jì)漲跌幅為： %.3f" %(accuDrop))
  elif self.stock_num == 100: self.sell(currentprice, 100)
  else: self.holdstock(currentprice)
  else: self.holdstock(currentprice)
 else: self.enter(currentprice,ex_price,accuDrop)

首先構(gòu)建了droplog專門用于計(jì)算累計(jì)漲跌幅，當(dāng)其大于0時(shí)重置為0，每次購(gòu)買后也將其重置為0。當(dāng)?shù)笥?.04則買入，一下為流程圖（因?yàn)樽鲌D軟件Visustin為試用版所以有水印，兩個(gè)圖可以結(jié)合來(lái)看）：

在這里插入圖片描述

此策略函數(shù)可以改成其他策略甚至是反馬丁，因?yàn)榻灰缀瘮?shù)可以通用。

作圖和輸出結(jié)果

buylog = pd.Series(broker.log)
close = data.copy()
buy = np.zeros(len(close))
sell = np.zeros(len(close))
for i in range(len(buylog)):
 if buylog[i] == 'buy':
 buy[i] = close[i]
 elif buylog[i] == 'sell':
 sell[i] = close[i]

buy = pd.Series(buy)
sell = pd.Series(sell)
buy.index = close.index
sell.index = close.index
quantlog = pd.Series(broker.quantlog)
quantlog.index = close.index
earn = pd.Series(broker.earn)
earn.index = close.index

buy = buy.loc[buy > 0]
sell = sell.loc[sell>0]
plt.plot(close)
plt.scatter(buy.index,buy,label = 'buy')
plt.scatter(sell.index,sell, label = 'sell')
plt.title('馬丁策略')
plt.legend()

#畫圖
plt.rcParams['font.sans-serif'] = ['SimHei']

fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(15,8))

ax1.plot(close)
ax1.scatter(buy.index,buy,label = 'buy',color = 'red')
ax1.scatter(sell.index,sell, label = 'sell',color = 'green')
ax1.set_ylabel('Price')
ax1.grid(True)
ax1.legend()

ax1.xaxis_date()
ax2.bar(quantlog.index, quantlog, width = 5)
ax2.set_ylabel('Volume')

ax2.xaxis_date()
ax2.grid(True)
ax3.xaxis_date()
ax3.plot(earn)
ax3.set_ylabel('總資產(chǎn)包括浮盈')
plt.show()

馬丁策略回測(cè)（中通客車）