And a simple test script to run it:

import backtrader as bt
import sys

sys.path.append('/docker_stocks/bbwavelet')

from datetime import datetime
import backtrader.indicators as btind
from bbwaveletlifting import bbhaarlift, bbdaubechieslift

class firstStrategy(bt.Strategy):
    def __init__(self):
        self.startlevel = 1
        self.endlevel = 5
        self.dbsig = [None] * 10

        self.barcount = bbbarcounter(largedist=500, majordist=100, minordist=10, plotdistance=0.07)


        for i in range(self.startlevel, self.endlevel+1):
            threshamount = (self.endlevel+1-i)/1000
            threshlist = [threshamount] * (self.endlevel)

            self.dbsig[i] = bbdaubechieslift(self.data.close, threshall=threshamount, threshtype = "S", donoholev = 3, donflag=True, subplot=True)

            #Another example usage:  self.dbsig[i] = bbdaubechieslift(self.data.close, threshlist = [0.5, 0.5, 0.5, 0.5, 0.5], threshtype = "S", donoholev = 3, donflag=True, subplot=True)





# Variable for our starting cash
startcash = 10000

# Create an instance of cerebro
cerebro = bt.Cerebro()

# Add our strategy
cerebro.addstrategy(firstStrategy)

# Get Apple data from Yahoo Finance.
data = bt.feeds.YahooFinanceData(
    dataname='AAPL',
    fromdate=datetime(2017, 4, 1),
    todate=datetime(2019, 4, 10),
    buffered=True
)

# Add the data to Cerebro
cerebro.adddata(data)

# Set our desired cash start
cerebro.broker.setcash(startcash)

# Run over everything
cerebro.run()

# Get final portfolio Value
portvalue = cerebro.broker.getvalue()
pnl = portvalue - startcash

# Print out the final result
print('Final Portfolio Value: ${}'.format(portvalue))
print('P/L: ${}'.format(pnl))

# Finally plot the end results
cerebro.plot(style='candlestick')

'''
Author: B. Bradford adapted from instructional information at 
        bearcave.com/Ian Kaplan.

MIT License

Copyright (c) B. Bradford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''

class bbdaubechieslift(bt.indicators.PeriodN):

    sqrt3 = math.sqrt(3)
    sqrt2 = math.sqrt(2)

    plotinfo = dict(subplot=True)
    lines = ('dblift', )
    plotlines = dict(haarlift=dict(alpha=0.75, ls='-', linewidth=1.0))
    params = (('coeffpick', None),
              ('donoholev', 1),
              ('donflag', True),
              ('threshlist', []),
              ('threshall', None),
              ('threshtype', "H"), )      #Hard or soft thresholding rules  2**coefflev data points required or 2**coefflev

    @staticmethod
    def next_power_of_2(x):
        return 1 if x == 0 else 2 ** (x.bit_length() -1)



    def init(self):
        self.addminperiod(2 ** self.params.coefflev + 1)

    def once(self, start, end):
        self.blnforward = 1
        self.blninverse = 2

        #create dummy line to manipulate and desired 2** data length (max or selected)
        lstinputdata = self.data.array[:]                                   #make a copy of the list/array

        #db is padded out in the case of None coefflev rather than truncated
        pow2 = self.next_power_of_2(len(lstinputdata)) * 2  # input needs to be power of two data points pad up to next pow2
        

        #Count backwards through line to leave most recent value in place if incremental: -- not provided here
        
        inlist = lstinputdata[:]

        #mirror inlist to avoid edge discontinuities -- make sure it's even length for daubechies:
        
        mirlistlen = len(self.data.array)
        mirpostlen = int((pow2 - len(lstinputdata))/2)
        mirprelen = pow2 - mirlistlen - mirpostlen
        mirlist =  inlist[:mirprelen][::-1] + inlist[:mirlistlen] + inlist[:-mirpostlen-1:-1]

        ftresult = bbdaubechieslift.forwardtrans(self, vec=mirlist)            #forward transform
        #threshedresult = ftresult
        threshedresult = bbdaubechieslift.wavshrink(self, coeffary=ftresult, threshall=self.p.threshall, threshtype=self.p.threshtype, threshlist=self.params.threshlist, donoholev = self.p.donoholev, donflag=self.p.donflag)
        #threshedresult = ftresult[:]       #if you don't want to threshold/wavshrink/donoho
        itresult = bbdaubechieslift.inversetrans(self, vec=threshedresult)    #inverse transform

        #demirror array:
        dummyaryout = itresult[mirprelen:mirlistlen + mirprelen]


        lstinputdata = dummyaryout[:]                                      # self.lines[0].array[pow2padding:] + itresult

        #Only run once if not doing incremental -- incremental not provided here.

        self.lines.dblift.array = lstinputdata       #arr.array('d', [lstinputdata[lstinphalf]] * (len(lstinputdata) - lstinphalf)) + lstinputdata[lstinphalf:]



    def predict(self, vec, N, direction):
        half = N >> 1

        if (direction == self.blnforward):
            #unnecessarily aggressive floats to avoid casting as int or concatenating strings:
            vec[half] = float(vec[half]) - (self.sqrt3/4.0)*float(vec[0]) - (((self.sqrt3-2.0)/4.0)*float(vec[half-1]))
        elif (direction == self.blninverse):
            vec[half] = float(vec[half]) + (self.sqrt3/4.0)*float(vec[0]) + (((self.sqrt3-2.0)/4.0)*float(vec[half-1]))
        else:
            debug.print("daubechies:predict: bad direction value")


        # predict, forward
        for n in range (0, half, 1):
            if (direction == self.blnforward):
                vec[half+n] = float(vec[half+n]) - (self.sqrt3/4.0)*float(vec[n]) - (((self.sqrt3-2.0)/4.0)*float(vec[n-1]))
            elif (direction == self.blninverse):
                vec[half+n] = float(vec[half+n]) + (self.sqrt3/4.0)*float(vec[n]) + (((self.sqrt3-2.0)/4.0)*float(vec[n-1]))
            else:
                debug.print("daubechies: predict: bad direction value")
                break
        return vec


    def updateone(self, vec, N, direction):
        half = N >> 1
        for n in range(0, half):
            updateVal = bbdaubechieslift.sqrt3 * float(vec[half+n])

            if (direction == self.blnforward):
                vec[n] = float(vec[n]) + updateVal
            elif (direction == self.blninverse):
                vec[n] = float(vec[n]) - updateVal
            else:
                debug.print("daubechies:updateone: bad direction value")
                break
        return vec


    def update(self, vec, N, direction):
        half = N >> 1

        for i in range (0, half-1, 1):
            if (direction == self.blnforward):
                vec[i] = float(vec[i]) - float(vec[half + i + 1])
            elif (direction == self.blninverse):
                vec[i] = float(vec[i]) + float(vec[half + i + 1])
            else:
                debug.print("haar update: bad direction value")
                break

        if (direction == self.blnforward):
            vec[half-1] = float(vec[half-1]) - float(vec[half])
        elif (direction == self.blninverse):
            vec[half-1] = float(vec[half-1]) + float(vec[half])
        return vec


    def forwardtrans(self, vec):
        
        #The result of forwardTrans is a set of wavelet coefficients
        #ordered by increasing frequency and an approximate average
        #of the input data set in vec.  
        

        N = len(vec)
       
        # set for counter:
        n = N
        while n > 1:
            # beware, vec is altered in-place
            bbdaubechieslift.split(vec, n)
            bbdaubechieslift.updateone(self, vec, n, self.blnforward)
            bbdaubechieslift.predict(self, vec, n, self.blnforward)
            bbdaubechieslift.update(self, vec, n, self.blnforward)
            bbdaubechieslift.normalize(self, vec, n, self.blnforward)
            n = n >> 1
        return vec

    def inversetrans(self, vec):
        N = len(vec)
        n = 2
        while n <= N:
            # beware, vec is altered in-place
            bbdaubechieslift.normalize(self, vec, n, self.blninverse)
            bbdaubechieslift.update(self, vec, n, self.blninverse)
            bbdaubechieslift.predict(self, vec, n, self.blninverse)
            bbdaubechieslift.updateone(self, vec, n, self.blninverse)
            bbdaubechieslift.merge(self, vec, n)
            n = n << 1
        return vec


    def calcdonoho(self, coeffary, donoholev, accpos):

        n = len(coeffary)
        #donoholev -- Get donoho coeffs to threshold from this wavelet level

        dcoeffstart = -int(sum(accpos[-donoholev:]))
        if -donoholev + 1 == 0:
            coefflist = coeffary[dcoeffstart:]
        else:
            dcoeffend = -int(sum(accpos[-donoholev + 1:]))
            coefflist = coeffary[dcoeffstart:dcoeffend]

        # noise variance sigma from dh is a numeric scalar representing (an estimate of) the additive Gaussian white noise variance.
        # Estimate the noise variance based on the median absolute deviation (MAD) of the scale one wavelet coefficients.
        n = len(coefflist)
        dh = median(coefflist)
        mad = median([abs(x - dh) for x in
                      coefflist])  # list(map(lambda x: abs(x-dh), coefflist)))      #difference from median for 1st scale coeffs
        # sigma = list(map(abs, diff))
        sigma = mad / 0.6745  # noise standard deviation
        donoho = math.sqrt(2 * math.log(n)) * sigma
        print ('donoho is:  ', donoho)
        return donoho

    def normalize(self, vec, N, direction):
        half = N >> 1
        for n in range(0, half):
            if direction == self.blnforward:
                vec[half + n] = float(vec[half + n]) - (bbdaubechieslift.sqrt3 / 4.0) * float(vec[n]) - (
                ((bbdaubechieslift.sqrt3 - 2) / 4.0) * float(vec[n - 1]))
            elif (direction == self.blninverse):
                vec[half + n] = float(vec[half + n]) + (bbdaubechieslift.sqrt3 / 4.0) * float(vec[n]) + (
                ((bbdaubechieslift.sqrt3 - 2.0) / 4.0) * float(vec[n - 1]))
            else:
                print("daubechies:normalize: bad direction value")
                break
        return vec


    def wavshrink(self, coeffary, threshlist, threshall, threshtype, donoholev, donflag=True):
       
        accpos = list()
        accpos += [math.floor((len(coeffary) - 1) / 2) + 2]

        while sum(accpos) <= len(coeffary):
            accpos.insert(0, math.floor((accpos[0] - 1) / 2) + 2)
        accpos = accpos[1:]


        donoho = self.calcdonoho(coeffary, donoholev = self.p.donoholev, accpos=accpos)

        if threshlist:
            for i in range(len(threshlist)-1,-1,-1):
                sumaccpos = -int(sum(accpos[i:]))
                sumaccposend = -int(sum(accpos[i + 1:]))
                if donflag:
                    donohomult = donoho * threshlist[i]
                    if donohomult:
                        for x in range(sumaccpos, sumaccposend):
                            print ("x is : ", x)

                            if threshtype.upper() == "S":
                                if abs(coeffary[x]) < donohomult:
                                    coeffary[x] = 0
                                elif coeffary[x] < -donohomult:
                                    coeffary[x] += donohomult
                                elif coeffary[x] > donohomult:
                                    coeffary[x] -= donohomult
                            elif threshtype.upper() == "H":
                                if abs(coeffary[x]) < donohomult:
                                    coeffary[x] = 0
                else:
                    for x in range(sumaccpos, sumaccposend):
                        coeffary[x] = float(coeffary[x]*threshlist[i])


            threshedresult = coeffary[:]


        #versus thresholding all coefficients:

        if threshall is not None:
            if donflag:
                threshval = donoho * threshall
            else:
                threshval = threshall
                
            if threshval:
                print("threshval is: ", threshval)
                for x in range(len(coeffary)):
                    if threshtype.upper() == "S":
                        if abs(coeffary[x]) < threshval:
                            coeffary[x] = 0
                        if coeffary[x] < -threshval:
                            coeffary[x] += threshval
                        elif coeffary[x] > threshval:
                            coeffary[x] -= threshval
                    elif threshtype.upper() == "H":
                        if abs(coeffary[x]) < threshval:
                            coeffary[x] = 0
            threshedresult = coeffary[:]



        return threshedresult




    def split(vec, N):
        start = 1
        end = N - 1

        while (start < end):
            #Could do this with list comprehension, but want to stick to the original structure:
            for i in range(start, end, 2):
                tmp = vec[i]
                vec[i] = vec[i + 1]
                vec[i+1] = tmp
            start += 1
            end -= 1

        return vec

    def merge(self, vec, N):
        half = N >> 1
        start = half-1
        end = half

        while (start > 0):
            for i in range(start, end, 2):
                tmp = vec[i]
                vec[i] = vec[i+1]
                vec[i+1] = tmp
            start -= 1
            end += 1

        return vec

Here's an indicator (bbdaubechieslift) that provides a Daubechies D4 wavelet transform via lifting for deconstructing a line into frequency sub-bands. It provides calculation of the Donoho threshold amount to minimize theoretical noise. Again, as with the other wavelet indicators, my general goal has been to comply with Daniel's R. vision of minimization of libraries. There is a math.floor library reference but this is easily coded around if desired.

Because this routine pads outwards to nearest 2**n bars, rather than getting a slice of the line, an incremental option isn't included. However, this padding out with symmetrical data does reduce edge effects/discontinuities in the Daubechies D4.

Example usage:

x = bbdaubechieslift(self.data.close, threshall=threshamount, threshtype = "S", donoholev = 3, donflag=True, subplot=True)

or, another way to threshold individual subbands:

x = bbdaubechieslift(self.data.close, threshlist = [0.5, 0.5, 0.5, 0.5, 0.5], threshtype = "S", donoholev = 3, donflag=True, subplot=True)

Threshlist and threshall are mutually exclusive.

donoholev specifies the subband to use for calculating the "optimum" theoretical noise filter -- usually the highest detail subband. Normally donoholev=1 however that doesn't always provide for the optimum noise reduction depending on the signal and the time frames of the forecast intended.

Threshall is the threshold multiplier for the calculated Donoho value which blanket thresholds all wavelet coefficients. The threshlist parameter can be supplied to threshold out individual coefficients for sub-bands, ie. threshlist = [0.5, 0.5, 0.5, 0.5, 0.5]. Please understand that in Daubechies D4 there is some overlap in signal between the bands and thresholding some bands and not others can result in erratic results. The Haar wavelet indicator I previously posted is fully orthogonal as an alternative.

It is extremely easy with this indicator to give it nonsensical parameters, for example a threshlist that is longer than the n in the data len of 2**n. Or a Donoho multiplier that is too low or too high. You may wish to experiment by monitoring the print output showing the threshval calculated by calcdonoho routine. A donoholev of 1 (the first level) will result in small noise reductions, whereas a donoho level calculated from higher levels results in much larger thresholding. If this is your first time, do not use hard thresholding (threshtype = "H") as it will be more difficult to form a coherent result without discontinuities.

I will have open hours available for contract work starting at the end of August. My inundated spam mailbox is bigdavediode2 at the email provider yahoo.com. Apologies for any bugs, but should there be some, and there probably are, at least this gives a good starting point for others to improve upon.

Typical output at multiple subband levels:

@amir-najafi66

Hi Amir, sorry for the delay in responding. There is no period as this pulls out highs and lows (peaks and valleys) where the high is highest compared to the previous low, and the subsequent low is lowest compared to the previous high so it starts from the line's beginning. A period would not work especially for situations such as [5,5,5,5,5,5,5] -- with five being ambiguous as to whether it's a high or a low or somewhere in between.

I haven't looked at this one, but perhaps this one will help? I tried to tighten an existing zigzag indicator up:

https://community.backtrader.com/topic/1390/zigzag-indicator

Although this routine is great for its simple flat-file data caching, it does have an implementation problem that can shut down the connection before the downloading of data is completed with its sleep statements. Another issue is that, even for small downloads, the sleep statements hold up processing and when one is trying to do analysis of 100-200 securities/hour these sleep statements have to be removed as below.

Ideally, the callback will just terminate the connection correctly when the "finished" response is received from the api. So I've altered the code below. It's a bit hard to follow due to the callbacks but appears to work effectively. Please note the timeoutsecs argument should be specified as it is the maximum that the routine will wait for the download.

from ib.opt import ibConnection, message
from ib.ext.Contract import Contract
from time import time, strftime
from datetime import datetime
import pandas as pd


class IBDataCache(object):

    def _reset_data(self, host='127.0.0.1', port=4001, client_id=None):
        self._df = pd.DataFrame(columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'OpenInterest'])
        self._s = pd.Series()

        #Initialize connection as long as it's not already connected:
        if (not hasattr(self, '_conn')) or (not self._conn.isConnected()):
            self._conn = ibConnection(host, port, client_id)
            self._conn.enableLogging()
            # Register the response callback function and type of data to be returned
            self._conn.register(self._error_handler, message.Error)
            self._conn.register(self._historical_data_handler, message.historicalData)
            self._conn.register(self._save_order_id, 'NextValidId')
            self._conn.register(self._nextValidId_handler, message.nextValidId)
            self._conn.connect()



    def _save_order_id(self, msg):
        self._next_valid_id = msg.orderId
        print('save order id',msg.orderId)

    def _nextValidId_handler(self, msg):
        print("nextValidId_handler: ", msg)
        self.inner(sec_type=self.req.m_secType, symbol=self.req.m_symbol, currency=self.req.m_currency, exchange=self.req.m_exchange, \
                   primaryExchange=self.req.m_primaryExch, endtime=self.req.endtime, duration=self.req.duration, \
                   bar_size=self.req.bar_size, what_to_show=self.req.what_to_show, use_rth=self.req.use_rth)

    def _error_handler(self, msg):
        print("error: ", msg)
        if not msg:
            print('disconnecting', self._conn.disconnect())



    def __init__(self, data_path='/docker_stocks/data', date_format='%Y%m%d %H:%M:%S', host='127.0.0.1', port=4001, client_id=None):
        self._data_path = data_path
        self._date_format = date_format
        self._next_valid_id = 1

        self._host = host
        self._port = port
        self._client_id = client_id






    def _historical_data_handler(self, msg):
        """
            Define historical data handler for IB - this will populate our pandas data frame
        """

        # print (msg.reqId, msg.date, msg.open, msg.close, msg.high, msg.low)
        if not 'finished' in str(msg.date):
            #print ("historical_data_handler: ", msg)
            try:
                self._s = ([datetime.strptime(msg.date, self._date_format),
                            msg.open, msg.high, msg.low, msg.close, msg.volume, 0])
            except:
                #for dates only with no time to str:
                self._s = ([datetime.strptime(msg.date, "%Y%m%d"),
                            msg.open, msg.high, msg.low, msg.close, msg.volume, 0])
            self._df.loc[len(self._df)] = self._s

        else:
            self._df.set_index('Date', inplace=True)



    def setAllWithKwArgs(self, **kwargs):
        #set all attributes to the kwargs to pass along
        for key, value in kwargs.items():
            setattr(self, key, value)

    def inner(self, sec_type, symbol, currency, exchange, primaryExchange, endtime, duration, bar_size, what_to_show, use_rth):
        print ("calling inner... setting up req.")
        self.req = Contract()
        self.req.m_secType = sec_type
        self.req.m_symbol = symbol
        self.req.m_currency = currency
        self.req.m_exchange = exchange
        self.primaryExch = primaryExchange
        self.req.endtime = endtime
        self.req.duration = duration
        self.req.bar_size = bar_size
        self.req.what_to_show = what_to_show
        self.req.use_rth = use_rth


        self._conn.reqHistoricalData(self._next_valid_id, self.req, endtime, duration,
                                     bar_size, what_to_show, use_rth, 1)


    def get_dataframe(self, sec_type, symbol, currency, exchange, primaryExchange, endtime, duration, bar_size, what_to_show, use_rth, timeoutsecs):



        # build filename
        self.filename = symbol + '_' + sec_type + '_' + exchange + '_' + currency + '_' + \
            endtime.replace(' ', '') + '_' + duration.replace(' ', '') + '_' + bar_size.replace(' ', '') + '_' + \
            what_to_show + '_' + str(use_rth) + '.csv'
        self.filename = self.filename.replace('/', '.')
        self.filename = self._data_path + '/' + self.filename
        print ("filename:  ", self.filename)


        try:

            # check if we have this cached
            self._df = pd.read_csv(self.filename,
                         parse_dates=True,
                         index_col=0)
            self._df.index = pd.to_datetime(self._df.index, format='%Y-%m-%d %H:%M:%S')
        except IOError:
            #set up connection:
            self._reset_data(self._host, self._port, self._client_id)

            # Not cached. Download it.
            # Establish a Contract object and the params for the request
            self.inner(sec_type, symbol, currency, exchange,
                  primaryExchange, endtime, duration, bar_size,
                  what_to_show, use_rth)

            # Make sure the connection doesn't get disconnected prior the response data return
            timeout = time() + timeoutsecs
            while self._conn.isConnected() and time() < timeout:
                #print(".", end="", flush=True)
                pass
            self._conn.disconnect()
            self._df.to_csv(self.filename)


        return self._df





if __name__ == '__main__':

    date_format = '%Y%m%d %H:%M:%S'

    downloader_kwargs = dict(
        data_path='../data',
        date_format=date_format,
        host='127.0.0.1',
        port=4001,
        client_id=992
    )
    downloader = IBDataCache(**downloader_kwargs)

    stock_kwargs = dict(
        sec_type='STK',
        symbol='AAPL',
        currency='USD',
        exchange='SMART',
        primaryExchange='NASDAQ',
        endtime=datetime(2018, 10, 26, 15, 59).strftime(date_format),
        duration='2 D',
        bar_size='30 mins',
        what_to_show='TRADES',
        use_rth=1
    )

    df = downloader.get_dataframe(**stock_kwargs)
    print(df)


    stock_kwargs = dict(
        sec_type='STK',
        symbol='MSFT',
        currency='USD',
        exchange='SMART',
        primaryExchange='NASDAQ',
        endtime=datetime(2018, 10, 26, 15, 59).strftime(date_format),
        duration='1 D',
        bar_size='1 min',
        what_to_show='TRADES',
        use_rth=1
    )

    df = downloader.get_dataframe(**stock_kwargs)

    print ("IBCacheData")

    print(df)

(As a side note, the int64 error mentioned above is due to the sleep statement from the original code terminating before the downloading is completed.)

@søren-pallesen

Hi, yes, among many other items I've created a rolling VWAP for backtrader which I've found useful. And as a challenge to myself I did it in four actual lines of code. If you use this you need to pay me by shipping me a box of your local delicacies (although not dried fish or lutefisk).

Investopedia has some good entries on VWAP and MVWAP as I recall.

'''
Author: B. Bradford 

MIT License

Copyright (c) B. Bradford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

That they contact me for shipping information for the purpose of sending a 
local delicacy of their choice native to whatever region they are domiciled.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


class VolumeWeightedAveragePrice(bt.Indicator):
    plotinfo = dict(subplot=False)

    params = (('period', 30), )

    alias = ('VWAP', 'VolumeWeightedAveragePrice',)
    lines = ('VWAP',)
    plotlines = dict(VWAP=dict(alpha=0.50, linestyle='-.', linewidth=2.0))



    def __init__(self):
        # Before super to ensure mixins (right-hand side in subclassing)
        # can see the assignment operation and operate on the line
        cumvol = bt.ind.SumN(self.data.volume, period = self.p.period)
        typprice = ((self.data.close + self.data.high + self.data.low)/3) * self.data.volume
        cumtypprice = bt.ind.SumN(typprice, period=self.p.period)
        self.lines[0] = cumtypprice / cumvol

        super(VolumeWeightedAveragePrice, self).__init__()

@ard9000 Without putting in an hour on your code I can tell you two things that I've found in my experience have been difficult for me. The first is that stop losses are tricky to implement properly and often they won't show any trades at all, for example, if a position is already opened and the code, hypothetically, only allows one open position at a time. The stop trail would not update when, as one example, there was an issue being called during next at a bar, and thus no trades would subsequently open as there was already a position open.

Another issue was capturing the errors from the data provider, in my case, IB. When I used the standard method of retrieving data, returned data provider error codes were not broken out in debug mode. For example, in your case this could be because you haven't sufficiently specified the exchange/primaryexchange. In that event this could mask the problem, or perhaps obtain varying data from different exchanges on the same instrument due to smart routing.

In my case (with IB) I cache data directly from an IBDataCache object to a pandas dataframe (just search for IB data cache on here if you want to look at this) which facilitated monitoring data provider errors/return codes, such as exchange, and then adjust the primaryexchange or other required data provider api arguments appropriately to specify a particular exchange.

Hi Kevin --

Another option for you is to use the IB data cache code that brettelliot wrote at https://community.backtrader.com/topic/1007/how-to-cached-data-from-ib-and-use-it-later/2

It exposes the IB connection so that the ib api parameter for RTH is available (use_rth).

Also gets you a nice cache and a bit of a speedup as well.

B.

Your code is extremely useful when dealing with IB's down times. And it's a nice speedup as well.

Just one quick suggestion/bug fix -- I've found that sometimes there's a numpy int64 error generated by line 268 in pandafeed (AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime') due to the fact that some datetimes are stored in int64 in pandas and need to be converted manually. The line I've added to alleviate this is:

        try:

            # check if we have this cached
            self._df = pd.read_csv(filename,
                         parse_dates=True,
                         index_col=0)
            self._df.index = pd.to_datetime(self._df.index, format='%Y-%m-%dT%H:%M:%S')

More information on this datetime conversion can be found here: https://community.backtrader.com/topic/676/bug-using-pandas-hdf

Again, great work brettelliot.