B
Yeah, his QTA book is a good book to study and it makes sense to me.
For continueITDDAnalyzer:
I have created one analyzer called ITDDAnalyzer and another called continueITDDAnalyzer. Former one is to compute ITDD at the end of ONE simulation while continueITDDAnalyzer compute succeeding simulations.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import backtrader as bt
from backtrader.analyzers import returns as returns
import pandas as pd
from backtrader.analyzers import TimeReturn
import logging
class ITDDAnalyzer(bt.TimeFrameAnalyzerBase):
def CalculateITDD(self):
value = self.strategy.broker.getvalue()
if value > self.peak:
self.peak = value
self.ddlen = 0 # start of streak
self.dd=dd= 100.0 * (self.peak - value) / self.peak
self.ddlen+=bool(dd)
self.ITDD=max(self.ITDD, self.dd)
self.ITDDLength=max(self.ITDDLength,self.ddlen)
def start(self,*args,**kwargs):
self.ITDD=0
self.ITDDLength=0
self.initV=self.strategy.broker.getvalue()
self.peak=float('-inf')
self.ddlen= -1
self.dd = 0
self.ITDDRecord=[]
def __init__(self):
self._returns = TimeReturn(timeframe=bt.TimeFrame.Months)
def notify_trade(self,trade):
if self.strategy.getposition(self.data).size==0:
#Calculate the ITDD together with commission included
self.CalculateITDD()
self.CARreturn=(self.strategy.broker.getvalue()/self.initV - 1)*100*252/max(trade.barlen,1)
self.ITDDRecord.append([self.ITDD, self.ITDDLength,self.CARreturn])
#Reset Parameters for next trade
self.ITDD=0
self.ITDDLength=int(0)
self.initV=self.strategy.broker.getvalue()
self.peak=float('-inf')
self.ddlen= -1
self.dd = 0
def on_dt_over(self):
if self.strategy.getposition(self.data).size==0:
self.CalculateITDD()
class continueITDDAnalyzer(ITDDAnalyzer):
def __init__(self):
self._returns = TimeReturn(timeframe=bt.TimeFrame.Months)
self.ITDDSet=self.strategy.contITDDSet
def start(self):
self.ITDD=self.ITDDSet['ITDD']
self.ITDDLength=self.ITDDSet['ITDDLength']
self.initV=self.ITDDSet['initV']
self.value=self.strategy.broker.getvalue()
self.peak=float(self.ITDDSet['peak'])
self.ddlen=self.ITDDSet['ddlen']
self.dd =self.ITDDSet['dd']
def get_analysis(self):
ITDDSet = {'ITDD':self.ITDD,
'ITDDLength':self.ITDDLength,
'initV':self.initV,
'value':self.strategy.broker.getvalue(),
'peak':self.peak,
'ddlen':self.ddlen,
'dd':self.dd}
return ITDDSet
For SizingStra, it is a simple strategy to buy and sell. One point to note is that the BuyPrice and SellPrice were pre-recorded in previous WFA simulation (tf or tradeFeed in below code). It has its own buy/sell price instead of bar open/close bar price particularly when strategy has trail stop that is NOT using bar close price to close the transaction in previous trading. I did it on purpose giving I want to have monte on my trade results. I extracted the most important part for your reference as well.
def __init__(self,targetRatio,tradeFeed):
self.historicOrder=[]
self.totalCommission=0 # This is to record total commission paid in simulation
self.o=dict() # orders per data (main, stop, limit, manual-close)
self.targetRatio = targetRatio #Target ratio is to calculate the fraction
self.tf= tradeFeed
def next(self):
self.analyzers.contITDD.CalculateITDD()
if (self.tf.ix[self.datas[0].datetime.datetime()]['BuyPrice'])>0:
PValue=self.broker.getvalue()
calSize=int(PValue*self.targetRatio/self.datas[0].open[0])
targetSize=max(calSize,0)
BuyPrice = self.tf.ix[self.datas[0].datetime.datetime()]['BuyPrice']
oneSimLogger.debug('PValue:{}, TargetSize:{}, BuyPrice:{}'.format(self.broker.getvalue(),targetSize,BuyPrice))
self.o = [self.buy(data=self.datas[0],exectype=bt.Order.Limit, size=targetSize,price=BuyPrice)]
elif (self.tf.ix[self.datas[0].datetime.datetime()]['SellPrice'])>0:
pos = self.getposition(self.data).size
SellPrice = self.tf.ix[self.datas[0].datetime.datetime()]['SellPrice']
oneSimLogger.debug('PValue:{}, TargetSize:{}, SellPrice:{}'.format(self.broker.getvalue(),pos,SellPrice))
self.o = [self.sell(data=self.datas[0],size=pos,exectype=bt.Order.Stop,price=SellPrice)]
