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duni4i

Hi! I'm looking for teacher/coach for backtrading. If you can help me please connect with me in Telegram @duni4i or skype.

duni4i

@run-out said in indicator on resampled data:

self.zz = ZigZag(self.datas[1], period=2, retrace=0.05, minbars=2)

Hi! I've done everything as you had adviced and i have data for datas[1], but nothing is calculated for datas[0]. COuld you advice something to solve it?

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose1 = self.datas[0].close
        self.dataclose30 = self.datas[1].close
        # To keep track of pending orders
        self.order = None
        self.zigzag1 = ZigzagNiko.ZigZag(self.datas[0], period=2, retrace=0.05, minbars=2)
        self.zigzag30 = ZigzagNiko.ZigZag(self.datas[1], period=2, retrace=0.05, minbars=2)
        self.ZZLevels1 = pd.DataFrame(columns = ['ZZ1'])
        self.ZZLevels30 = pd.DataFrame(columns = ['ZZ30'])

and second part when i check data is:

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close1, %.2f' % self.dataclose1[0])
        self.log('Close30, %.2f' % self.dataclose30[0])
        if self.zigzag30.lines.zigzag[0]>0 or self.zigzag1.lines.zigzag[0]>0:
            self.log('ZZ30, %.2f' % self.zigzag30.lines.zigzag[0])
            self.log('ZZ10, %.2f' % self.zigzag1.lines.zigzag[0])
        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return
        df1 = pd.DataFrame({'ZZ1': [self.zigzag1.lines.zigzag[0]]})
        df30 = pd.DataFrame({'ZZ30': [self.zigzag30.lines.zigzag[0]]})
        self.ZZLevels30=self.ZZLevels30.append(df30, ignore_index=True)
        self.ZZLevels30=self.ZZLevels30.dropna()
        self.ZZLevels1=self.ZZLevels1.append(df1, ignore_index=True)
        self.ZZLevels1=self.ZZLevels1.dropna()
        print(self.ZZLevels30)
        print(self.ZZLevels1)

and finally i add data this way:

    data = exporter.download(asset_id, market=Market.FUTURES, timeframe=Timeframe.MINUTES1, start_date=fromdate1)
    data['<DATE>'] = data['<DATE>'].apply(lambda x: str(x))
    data['<TIME>'] = data['<TIME>'].apply(lambda x: str(x))
    data['<DATE>'] =data['<DATE>']+' '+ data['<TIME>']
    data['<DATE>'] = data['<DATE>'].apply(lambda x: pd.to_datetime(str(x), format='%Y-%m-%d %H:%M:%S.%f'))
    rts = pd.DataFrame(data)
    rts = rts.set_index('<DATE>')
    rts= rts.loc[:,['<OPEN>','<HIGH>', '<LOW>', '<CLOSE>','<VOL>']]
    rts.index.names = ['Date']
    rts.columns=['Open','High','Low','Close','Volume']
    #print(rts)
    data1 = bt.feeds.PandasData(dataname=rts)
    cerebro.adddata(data1,name = '1M')
    # Add the Data Feed to Cerebro
    data30 = cerebro.resampledata(data1, name='data30', timeframe=bt.TimeFrame.Minutes, compression = 30)
    # Add the Data Feed to Cerebro
    cerebro.adddata(data30,name = '30M')

and finally i have data for ZZ30 and nothing for ZZ1:

duni4i

@asus-rog Have you tried to use it for 2 timeframes? I've tried with resampling in backtrader, resampling in pandas, but nothing helps. Everytime I've got just Nan.

duni4i

@j-t Hi! I have some issue, have you found a decision? As I've seen in Nikolai's post this indicator is for one timeframe. I try to change it but nothing works.

duni4i

Hi, Nikolai!
Could you please advice how to use your indicator for 2 timeframes?

duni4i

@vladisld perhaps I'm not pretty correct but after loading 2 timeframes i see correct data for my indicator when I debug each step and I see an array with correct calculations but it is zero because when i add second timeframe indicator starts to recalculate every time when next starts and all previous data are Nan. SO my question is how to call proper calculations from my indicator and not allow it to recalculate?

duni4i

@vladisld Thank you. Now data is loaded but indicator still does not work and make all data 'Nan'

duni4i

@dasch thanks for your respond, but it did not help me. Now python does not understand what i do

duni4i

Hi to everyone!
I've got a problem that i can't resolve for some time.
I use custon indicator in my code. It works perfectly with one time frame data, but it completely does not work with two of them.
I initialize both timeframes in init

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose1 = self.datas[0].close
        self.dataclose30 = self.datas[1].close

        # To keep track of pending orders
        self.order = None
        #это расчет зигзага по таймфрейму 1 минута
        self.zigzag1 = Zigzag1.ZigZag(self.dataclose1)
        self.SMA = bt.indicators.SmoothedMovingAverage(self.dataclose1, period=10)
        #это расчет зигзага по таймфрейму 30 минут
        self.zigzag30 = Zigzag1.ZigZag(self.dataclose30)
        self.SMA30 = bt.indicators.SmoothedMovingAverage(self.dataclose30, period=30)
        self.ZZLevels1 = pd.DataFrame(columns = ['ZZ1','Index_in_Data1'])
        self.ZZLevels30 = pd.DataFrame(columns = ['ZZ30','Index_in_Data30'])

After that i try to call lines of zigzag in my next part:

   def next(self):
        if self.order:
            return

        df1 = pd.DataFrame({'ZZ1': [self.zigzag1.lines.zigzag_peak[0]],'Index_in_Data1': [self.zigzag1.lines.last_pivot_t[0]]})
        df30 = pd.DataFrame({'ZZ30': [self.zigzag30.lines.zigzag_peak[0]],'Index_in_Data30': [self.zigzag30.lines.last_pivot_t[0]]})

and here is when i print result I've got nothing. I thought that the problem is with data and i've checked everything with SMA indicator, and SMA was calculated correctly for both timeframes.

And here is I create two timeframes data.

cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)
    ticker = 'RTS'
    exporter = Exporter()
    asset = exporter.lookup(name=ticker, market=Market.FUTURES)
    asset_id = asset[asset['name'] == ticker].index[0]
    fromdate1=datetime.date(2021, 7, 1)
    data = exporter.download(asset_id, market=Market.FUTURES, timeframe=Timeframe.MINUTES1, start_date=fromdate1)
    data['<DATE>'] = data['<DATE>'].apply(lambda x: str(x))
    data['<TIME>'] = data['<TIME>'].apply(lambda x: str(x))
    data['<DATE>'] =data['<DATE>']+' '+ data['<TIME>']
    data['<DATE>'] = data['<DATE>'].apply(lambda x: pd.to_datetime(str(x), format='%Y-%m-%d %H:%M:%S.%f'))
    rts = pd.DataFrame(data)
    rts = rts.set_index('<DATE>')
    rts= rts.loc[:,['<OPEN>','<HIGH>', '<LOW>', '<CLOSE>','<VOL>']]
    rts.index.names = ['Date']
    rts.columns=['Open','High','Low','Close','Volume']
    #print(rts)
    data1 = bt.feeds.PandasData(dataname=rts)
    print(data1)
    cerebro.adddata(data1,name = '1M')
    data30 = cerebro.resampledata(data1, name='data30', timeframe=bt.TimeFrame.Minutes, compression = 30)
    print(data30)
    # Add the Data Feed to Cerebro
    cerebro.adddata(data30,name = '30M')

I think that also you may need my code of zigzag:

import backtrader as bt
import datetime
import math

class ZigZag(bt.ind.PeriodN):
    '''
      Identifies Peaks/Troughs of a timeseries
    '''
    lines = (
        'trend', 'last_pivot_t', 'last_pivot_x', 'last_pivot_ago',
        'zigzag_peak', 'zigzag_valley', 'zigzag', 'last_zigzag',
    )

    # Fancy plotting name
    # plotlines = dict(logreturn=dict(_name='log_ret'))
    plotinfo = dict(
        subplot=False,
        plotlinelabels=True, plotlinevalues=True, plotvaluetags=True,
    )

    plotlines = dict(
        trend=dict(marker='', markersize=0.0, ls='', _plotskip=True),
        last_pivot_t=dict(marker='', markersize=0.0, ls='', _plotskip=True),
        last_pivot_x=dict(marker='', markersize=0.0, ls='', _plotskip=True),
        last_pivot_ago=dict(marker='', markersize=0.0, ls='', _plotskip=True),
        zigzag_peak=dict(marker='*', markersize=4.0, color='blue',
                         fillstyle='full', ls=''),
        zigzag_valley=dict(marker='*', markersize=4.0, color='green',
                           fillstyle='full', ls=''),
        zigzag=dict(_name='zigzag', color='blue', ls='-', _skipnan=True),
        last_zigzag=dict(_name='last_zigzag', color='blue', ls='--', _skipnan=True),
    )

    # update value to standard for Moving Averages
    params = (
        ('period', 9),
        ('up_retrace', 0.1),
        ('dn_retrace', 0.1),
        ('bardist', 0.02),  # distance to max/min in absolute perc
    )

    def __init__(self):
        super(ZigZag, self).__init__()

        if not self.p.up_retrace:
            raise ValueError('Upward retracement should not be zero.')

        if not self.p.dn_retrace:
            raise ValueError('Downward retracement should not be zero.')

        if self.p.up_retrace < 0:
            self.p.up_retrace = -self.p.up_retrace

        if self.p.dn_retrace > 0:
            self.p.dn_retrace = -self.p.dn_retrace

        self.p.up_retrace = self.p.up_retrace / 100
        self.p.dn_retrace = self.p.dn_retrace / 100

        self.missing_val = float('Nan')

    def prenext(self):
        self.lines.trend[0] = 0
        self.lines.last_pivot_t[0] = 0
        self.lines.last_pivot_x[0] = self.data[0]
        self.lines.last_pivot_ago[0] = 0
        self.lines.zigzag_peak[0] = self.missing_val
        self.lines.zigzag_valley[0] = self.missing_val
        self.lines.zigzag[0] = self.missing_val
        self.lines.last_zigzag[0] = self.missing_val

    def next(self):
        data = self.data
        trend = self.lines.trend
        last_pivot_t = self.lines.last_pivot_t
        last_pivot_x = self.lines.last_pivot_x
        last_pivot_ago = self.lines.last_pivot_ago
        zigzag_peak = self.lines.zigzag_peak
        zigzag_valley = self.lines.zigzag_valley
        zigzag = self.lines.zigzag
        last_zigzag = self.lines.last_zigzag

        x = data[0]
        r = x / last_pivot_x[-1] - 1
        curr_idx = len(data) - 1

        trend[0] = trend[-1]
        last_pivot_x[0] = last_pivot_x[-1]
        last_pivot_t[0] = last_pivot_t[-1]
        last_pivot_ago[0] = curr_idx - last_pivot_t[0]
        zigzag_peak[0] = self.missing_val
        zigzag_valley[0] = self.missing_val
        zigzag[0] = self.missing_val
        last_zigzag[0] = x


        if trend[-1] == 0:
            if r >= self.p.up_retrace:
                piv = last_pivot_x[0] * (1 - self.p.bardist)
                zigzag_peak[-int(last_pivot_ago[0])] = piv
                zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
                trend[0] = 1
                last_pivot_x[0] = x
                last_pivot_t[0] = curr_idx

            elif r <= self.p.dn_retrace:
                piv = last_pivot_x[0] * (1 + self.p.bardist)
                zigzag_peak[-int(last_pivot_ago[0])] = piv
                zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
                trend[0] = -1
                last_pivot_x[0] = x
                last_pivot_t[0] = curr_idx

        elif trend[-1] == -1:
            if r >= self.p.up_retrace:
                piv = last_pivot_x[0] * (1 - self.p.bardist)
                zigzag_peak[-int(last_pivot_ago[0])] = piv
                zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
                trend[0] = 1
                last_pivot_x[0] = x
                last_pivot_t[0] = curr_idx
            elif x < last_pivot_x[-1]:
                last_pivot_x[0] = x
                last_pivot_t[0] = curr_idx
        elif trend[-1] == 1:
            if r <= self.p.dn_retrace:
                piv = last_pivot_x[0] * (1 + self.p.bardist)
                zigzag_peak[-int(last_pivot_ago[0])] = piv
                zigzag[-int(last_pivot_ago[0])] = last_pivot_x[0]
                trend[0] = -1
                last_pivot_x[0] = x
                last_pivot_t[0] = curr_idx

            elif x > last_pivot_x[-1]:
                last_pivot_t[0] = curr_idx
                last_pivot_x[0] = x

        idx = 1
        while idx < len(self.zigzag) and math.isnan(zigzag[-idx]):
            last_zigzag[-idx] = self.missing_val
            idx += 1

        if idx < len(self.data):
            last_zigzag[-idx] = zigzag[-idx]

It would be great if you can advice me what to do to make my two dataframes zigzag working.
Please notice that you might have issue with loading data if you do not use FInam explorer. It is loaded easily with pip finamexporter:

from finam import Exporter, Market, LookupComparator,Timeframe

duni4i

@duni4i

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