Real-Time Plotting

[mics]

Has anyone had success in creating real-time plotting. Cerebro does not appear to be the solution here, however, it appears others have found a way that might leave clues on how to accomplish this.

A programmer (sendex) has previously created live stock plotting, however, this is back in the day when Yahoo finance was available. Here is his post:
https://pythonprogramming.net/advanced-matplotlib-graphing-charting-tutorial/

Does anyone have the skills to alter this code so that it draws data from stores like Interactive Brokers or OANDA, while still being able to leverage Backtrader for analysis?

FYI - I would replace the def rsiFunc, def movingaverage, def ExpMovingAverage and def computeMACD with the Backtrader, but before commencing this it would be good to know how to leverage IB or OANDA as the data source.

Can anyone help?

As far as I can tell the following section needs editing...

def graphData(stock,MA1,MA2):

        #Use this to dynamically pull a stock:

    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        stockFile =[]
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')

    try:
        date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
                                                              converters={ 0: bytespdate2num('%Y%m%d')})
    

FULL CODE (BY SENDEX) HERE:

# THIS VERSION IS FOR PYTHON 3 #
import urllib.request, urllib.error, urllib.parse
import time
import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib.finance import candlestick_ohlc
import matplotlib
import pylab
matplotlib.rcParams.update({'font.size': 9})

def rsiFunc(prices, n=14):
    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed>=0].sum()/n
    down = -seed[seed<0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1.+rs)

    for i in range(n, len(prices)):
        delta = deltas[i-1] # cause the diff is 1 shorter

        if delta>0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n-1) + upval)/n
        down = (down*(n-1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1.+rs)

    return rsi

def movingaverage(values,window):
    weigths = np.repeat(1.0, window)/window
    smas = np.convolve(values, weigths, 'valid')
    return smas # as a numpy array


def ExpMovingAverage(values, window):
    weights = np.exp(np.linspace(-1., 0., window))
    weights /= weights.sum()
    a =  np.convolve(values, weights, mode='full')[:len(values)]
    a[:window] = a[window]
    return a


def computeMACD(x, slow=26, fast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = ExpMovingAverage(x, slow)
    emafast = ExpMovingAverage(x, fast)
    return emaslow, emafast, emafast - emaslow


def bytespdate2num(fmt, encoding='utf-8'):
    strconverter = mdates.strpdate2num(fmt)
    def bytesconverter(b):
        s = b.decode(encoding)
        return strconverter(s)
    return bytesconverter

def graphData(stock,MA1,MA2):

    '''
        Use this to dynamically pull a stock:
    '''
    try:
        print('Currently Pulling',stock)
        urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
        stockFile =[]
        try:
            sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
            splitSource = sourceCode.split('\n')
            for eachLine in splitSource:
                splitLine = eachLine.split(',')
                if len(splitLine)==6:
                    if 'values' not in eachLine:
                        stockFile.append(eachLine)
        except Exception as e:
            print(str(e), 'failed to organize pulled data.')
    except Exception as e:
        print(str(e), 'failed to pull pricing data')

    try:
        date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True,
                                                              converters={ 0: bytespdate2num('%Y%m%d')})
        x = 0
        y = len(date)
        newAr = []
        while x < y:
            appendLine = date[x],openp[x],highp[x],lowp[x],closep[x],volume[x]
            newAr.append(appendLine)
            x+=1
            
        Av1 = movingaverage(closep, MA1)
        Av2 = movingaverage(closep, MA2)

        SP = len(date[MA2-1:])
            
        fig = plt.figure(facecolor='#07000d')

        ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
        candlestick_ohlc(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')

        Label1 = str(MA1)+' SMA'
        Label2 = str(MA2)+' SMA'

        ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5)
        ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
        
        ax1.grid(True, color='w')
        ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
        ax1.yaxis.label.set_color("w")
        ax1.spines['bottom'].set_color("#5998ff")
        ax1.spines['top'].set_color("#5998ff")
        ax1.spines['left'].set_color("#5998ff")
        ax1.spines['right'].set_color("#5998ff")
        ax1.tick_params(axis='y', colors='w')
        plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
        ax1.tick_params(axis='x', colors='w')
        plt.ylabel('Stock price and Volume')

        maLeg = plt.legend(loc=9, ncol=2, prop={'size':7},
                   fancybox=True, borderaxespad=0.)
        maLeg.get_frame().set_alpha(0.4)
        textEd = pylab.gca().get_legend().get_texts()
        pylab.setp(textEd[0:5], color = 'w')

        volumeMin = 0
        
        ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        rsi = rsiFunc(closep)
        rsiCol = '#c1f9f7'
        posCol = '#386d13'
        negCol = '#8f2020'
        
        ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
        ax0.axhline(70, color=negCol)
        ax0.axhline(30, color=posCol)
        ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
        ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
        ax0.set_yticks([30,70])
        ax0.yaxis.label.set_color("w")
        ax0.spines['bottom'].set_color("#5998ff")
        ax0.spines['top'].set_color("#5998ff")
        ax0.spines['left'].set_color("#5998ff")
        ax0.spines['right'].set_color("#5998ff")
        ax0.tick_params(axis='y', colors='w')
        ax0.tick_params(axis='x', colors='w')
        plt.ylabel('RSI')

        ax1v = ax1.twinx()
        ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4)
        ax1v.axes.yaxis.set_ticklabels([])
        ax1v.grid(False)
        ###Edit this to 3, so it's a bit larger
        ax1v.set_ylim(0, 3*volume.max())
        ax1v.spines['bottom'].set_color("#5998ff")
        ax1v.spines['top'].set_color("#5998ff")
        ax1v.spines['left'].set_color("#5998ff")
        ax1v.spines['right'].set_color("#5998ff")
        ax1v.tick_params(axis='x', colors='w')
        ax1v.tick_params(axis='y', colors='w')
        ax2 = plt.subplot2grid((6,4), (5,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
        fillcolor = '#00ffe8'
        nslow = 26
        nfast = 12
        nema = 9
        emaslow, emafast, macd = computeMACD(closep)
        ema9 = ExpMovingAverage(macd, nema)
        ax2.plot(date[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
        ax2.plot(date[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
        ax2.fill_between(date[-SP:], macd[-SP:]-ema9[-SP:], 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)

        plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
        ax2.spines['bottom'].set_color("#5998ff")
        ax2.spines['top'].set_color("#5998ff")
        ax2.spines['left'].set_color("#5998ff")
        ax2.spines['right'].set_color("#5998ff")
        ax2.tick_params(axis='x', colors='w')
        ax2.tick_params(axis='y', colors='w')
        plt.ylabel('MACD', color='w')
        ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
        for label in ax2.xaxis.get_ticklabels():
            label.set_rotation(45)

        plt.suptitle(stock.upper(),color='w')
        plt.setp(ax0.get_xticklabels(), visible=False)
        plt.setp(ax1.get_xticklabels(), visible=False)
        
        ax1.annotate('Big news!',(date[510],Av1[510]),
            xytext=(0.8, 0.9), textcoords='axes fraction',
            arrowprops=dict(facecolor='white', shrink=0.05),
            fontsize=14, color = 'w',
            horizontalalignment='right', verticalalignment='bottom')

        plt.subplots_adjust(left=.09, bottom=.14, right=.94, top=.95, wspace=.20, hspace=0)
        plt.show()
        fig.savefig('example.png',facecolor=fig.get_facecolor())
           
    except Exception as e:
        print('main loop',str(e))

while True:
    stock = input('Stock to plot: ')
    graphData(stock,10,50)

[mics]

For reference, here is my runstrategy() module that I want to integrate with 'real-time' plotting. It's a modified/integrated version of ibtest.py and iboandav20.py to suit my needs. Works fine with live and historical.

How could I place real-time plotting into this module, based on the sample code above, or by other means?

Thank in advance!!

#!/usr/local/bin/python
# -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

#----- IMPORT PACKAGES -----#
import argparse
import datetime
import backtrader as bt
import backtrader.indicators as btind
import pytz
from backtrader import Observer
from backtrader.utils import flushfile  # win32 quick stdout flushing
from twilio.rest import Client
import logging
from tabulate import tabulate
import time
#import numpy as np
#import matplotlib.pyplot as pyplot
#----- END -----#

#----- FOLDER DIRECTORY FOR MODULES -----#
from __modules__.coremodules import __directory__ as directory
import sys
sys.path.append(directory.parentfolder)
#----- END -----#

#----- IMPORT CUSTOM INDICATORS -----#
from coremodules.start import startcode
#from indicators.klingerosc import KlingerOSC
#from indicators.swing import SwingInd
from args.args import parse_args
from coremodules.tradeanalysis import TradeAnalysis
from coremodules.sqn import SQN
from coremodules.printstrategy import printstrategy
from observers.buysellplot import BuySellPlot
from coremodules.plotscheme import PlotScheme
import actions.sendsms

#----- END -----#


#----- OANDA STORE -----#
args = parse_args()

if args.oanda:
    import btoandav20
    from information.broker_oanda import oandakeys
    
    StoreCls = btoandav20.stores.OandaV20Store
    DataCls = btoandav20.feeds.OandaV20Data
    # BrokerCls = btoandav20.brokers.OandaV20Broker

    # available timeframes for oanda
    TIMEFRAMES = [bt.TimeFrame.Names[bt.TimeFrame.Seconds],
             bt.TimeFrame.Names[bt.TimeFrame.Minutes],
             bt.TimeFrame.Names[bt.TimeFrame.Days],
             bt.TimeFrame.Names[bt.TimeFrame.Weeks],
             bt.TimeFrame.Names[bt.TimeFrame.Months]]
             
elif args.ib is not None:
    from information.broker_ib import IBhost

    #----- END -----#

def runstrategy():
    args = parse_args()
    
    if args.ib and args.oanda is None:
        print('ERROR: Please select broker: args.oanda or args.ib')
        
#----- CEREBRO START -----#
    
    if args.optimise:
        cerebro = bt.Cerebro(optreturn=False)
    else:
        cerebro = bt.Cerebro()
    
#----- PLOT INFO -----#
    
    plotinfo = dict(plot=True,
         subplot=True,
         plotname='',
         plotskip=False,
         plotabove=False,
         plotlinelabels=True,
         plotlinevalues=True,
         plotvaluetags=True,
         plotymargin=0.0,
         plotyhlines=[],
         plotyticks=[],
         plothlines=[],
         plotforce=False,
         plotmaster=None,
         plotylimited=True,
    )

#----- SELECT STRATEGY -----#

    if args.strategy == 'reversal':
       from strategies.strategy_reversal import TheStrategy as TheStrategy
    elif args.strategy == '3_bar_stack':
       from strategies.strategy_3_bar_stack import TheStrategy as TheStrategy
    elif args.strategy == 'pivotpoint':
       from strategies.strategy_pivotpoint import TheStrategy as TheStrategy
    elif args.strategy == 'bom':
       from strategies.strategy_bom import TheStrategy as TheStrategy
    elif args.strategy == 'bband':
       from strategies.strategy_bband import TheStrategy as TheStrategy
    elif args.strategy == '2wave':
       from strategies.strategy_secondwave import TheStrategy as TheStrategy

#----- LOAD MODULES -----#

    def printTradeAnalysis(analyzer):
        return TradeAnalysis(analyzer)
        
    def printSQN(analyzer):          
        return SQN(analyzer)

#----- STORE & BROKER -----#

    if args.ib is not None:
        
        if args.usestore:
            ibstore = bt.stores.IBStore(**IBhost.storekwargs)

        if args.broker:
            if args.usestore:
                broker = ibstore.getbroker()
            else:
                broker = bt.brokers.IBBroker(**IBhost.storekwargs)

            cerebro.setbroker(broker)
            
        else:
            startcash = 1000000
            cerebro.broker.setcash(startcash)
            
    elif args.oanda:

        storekwargs = dict(
            token=args.token,
            account=args.account,
            practice=not args.live
        )

        if not args.no_store:
            store = StoreCls(**storekwargs)

        if args.broker:
            if args.no_store:
                broker = BrokerCls(**storekwargs)
            else:
                broker = store.getbroker()

            cerebro.setbroker(broker)
            
        else:
            startcash = 1000000
            cerebro.broker.setcash(startcash)

#----- TIMEFRAME AND COMPRESSION -----#
    if args.ib is not None:
    
        timeframe = args.timeframe
        # Manage data1 parameters
        tf1 = args.timeframe1
        #tf1 = bt.TimeFrame.TFrame(tf1) if tf1 is not None else timeframe
        cp1 = args.compression1
        cp1 = cp1 if cp1 is not None else args.compression
        '''
        if args.resample or args.replay:
            datatf = datatf1 = bt.Timeframe.Minutes
            datacomp = datacomp1 = args.compression
        else
            datatf = timeframe
            datacomp = args.compression
            datatf1 = tf1
            datacomp1 = cp1'''

        if args.resample or args.replay:
            datatf1 = datatf = args.timeframe
            datacomp1 = datacomp = 1
        else:
            datatf = timeframe
            datacomp = args.compression
            datatf1 = tf1
            datacomp1 = cp1

        if not args.historical:
            fromdate = datetime.datetime.now() - datetime.timedelta(days=4)
            todate = None
        else:
            dtformat = '%Y-%m-%d' + ('T%H:%M:%S' * ('T' in args.fromdate))
            fromdate = datetime.datetime.strptime(args.fromdate, dtformat)
            if args.now:
                todate = datetime.datetime.now()
            else:
                dtformat = '%Y-%m-%d' + ('T%H:%M:%S' * ('T' in args.todate))
                todate = datetime.datetime.strptime(args.todate, dtformat)

        if args.now:
            todate = datetime.datetime.now()
    
    elif args.oanda:
        timeframe = bt.TimeFrame.TFrame(args.timeframe)
        # Manage data1 parameters
        tf1 = args.timeframe1
        #tf1 = bt.TimeFrame.TFrame(tf1) if tf1 is not None else timeframe
        cp1 = args.compression1
        cp1 = cp1 if cp1 is not None else args.compression
        
        if args.resample or args.replay:
            datatf = datatf1 = bt.TimeFrame.Ticks
            datacomp = datacomp1 = 1
        else:
            datatf = timeframe
            datacomp = args.compression
            datatf1 = tf1
            datacomp1 = cp1

        fromdate = None
        if args.fromdate:
            dtformat = '%Y-%m-%d' + ('T%H:%M:%S' * ('T' in args.fromdate))
            fromdate = datetime.datetime.strptime(args.fromdate, dtformat)

#----- STORE AND DATAS - INTERACTIVE BROKERS -----#

    if args.ib is not None:
    
        IBDataFactory = ibstore.getdata if args.usestore else bt.feeds.IBData

        datakwargs = dict(
            timeframe=datatf,
            compression=datacomp,
            historical=args.historical,
            fromdate=fromdate,
            todate=todate,
            rtbar=args.rtbar,
            qcheck=args.qcheck,
            what=args.what,
            backfill_start=True,
            backfill=True,
            #backfill_start=not args.no_backfill_start,
            #backfill=not args.no_backfill,
            latethrough=args.latethrough,
            tz=args.timezone
        )

        if not args.usestore and not args.broker:   # neither store nor broker
            datakwargs.update(IBhost.storekwargs)  # pass the store args over the data

#----- STORE AND DATAS - OANDA -----#

    elif args.oanda:
        
        DataFactory = DataCls if args.no_store else store.getdata

        datakwargs = dict(
            timeframe=datatf, compression=datacomp,
            qcheck=args.qcheck,
            historical=args.historical,
            fromdate=fromdate,
            bidask=args.bidask,
            useask=args.useask,
            backfill_start=not args.no_backfill_start,
            backfill=not args.no_backfill,
            tz=args.timezone
        )

        if args.no_store and not args.broker:   # neither store nor broker
            datakwargs.update(storekwargs)  # pass the store args over the data


#--- DATAS - CSV FILE ---#
    
    if args.csv:
        
        datapath = '../data/csv/commodities/XAUUSD/XAUUSD_m1_Ask_2012-2016.csv'
        data0 = bt.feeds.GenericCSVData(
            timeframe=bt.TimeFrame.Minutes,
            compression=1,
            dataname=datapath,
            nullvalue=0.0,
            dtformat=('%m/%d/%Y'),
            tmformat=('%H:%M:%S'),
            fromdate=fromdate,
            todate=todate,
            datetime=0,
            time=1,
            high=3,
            low=4,
            open=2,
            close=5,
            volume=6,
            openinterest=-1 #-1 means not used
            )

#--- DATA 0 - INTERACTIVE BROKERS & OANDA  ---#
    else:
        if args.ib is not None:
            data0 = IBDataFactory(dataname=args.data0, **datakwargs)
        elif args.oanda:
            data0 = DataFactory(dataname=args.data0, **datakwargs)

#--- DATA 1 - BOTH INTERACTIVE BROKERS & OANDA  ---#

        data1 = None
        
        if args.data1 is not None:
            if args.data1 != args.data0:
                datakwargs['timeframe'] = datatf1
                datakwargs['compression'] = datacomp1
                data1 = IBDataFactory(dataname=args.data1, **datakwargs)
            else:
                data1 = data0

        rekwargs = dict(
            timeframe=timeframe, compression=args.compression,
            bar2edge=not args.no_bar2edge,
            adjbartime=not args.no_adjbartime,
            rightedge=not args.no_rightedge,
            takelate=not args.no_takelate,
        )
    
#----- LOAD DATA -----#
    
    if args.replay:
        cerebro.replaydata(data0, **rekwargs)

        if data1 is not None:
            rekwargs['timeframe'] = tf1
            rekwargs['compression'] = cp1
            cerebro.replaydata(data1, **rekwargs)

    elif args.resample:
        cerebro.resampledata(data0, **rekwargs)

        if data1 is not None:
            rekwargs['timeframe'] = tf1
            rekwargs['compression'] = cp1
            cerebro.resampledata(data1, **rekwargs)

    else:
        cerebro.adddata(data0)
        if data1 is not None:
            cerebro.adddata(data1)

    if args.valid is None:
        valid = None
    else:
        valid = datetime.timedelta(seconds=args.valid)


    #--- PROCESSING LOAD TIME ---#
    cerebro.dtcerebro = dt0 = datetime.datetime.now()
    print('Cerebro Start Time:          {}'.format(dt0))
    
#----- CEREBRO RUN - OPTIMISE -----#
# https://www.backtrader.com/blog/posts/2015-07-23-multicore-optimization/multicore-optimization/

    if args.optimise:
    
        #cerebro.addstrategy(TheStrategy, bomperiod_0=100)
        cerebro.optstrategy(TheStrategy, bomperiod_0=(50,100,300))
        #cerebro.adddata(data0)
        # Run over everything
        opt_runs = cerebro.run(maxcpus=1)
        
        # Generate results list
        final_results_list = []
        for run in opt_runs:
            for strategy in run:
                value = round(strategy.broker.getvalue(),2)
                pnl = round(value - startcash,2)
                period = strategy.params.bomperiod_0
                final_results_list.append([period,pnl])
                        
        #Sort Results List
        by_period = sorted(final_results_list, key=lambda x: x[0])
        by_PnL = sorted(final_results_list, key=lambda x: x[1], reverse=True)

        #Print results
        print('')
        print('Results: Ordered by period:')
        for result in by_period:
            print('Period: {}, PnL: {:5,.5f}'.format(result[0], result[1]))
        print('')
        print('Results: Ordered by Profit:')
        for result in by_PnL:
            print('Period: {}, PnL: {:5,.5f}'.format(result[0], result[1]))
        print('')
    
    
#----- CEREBRO RUN - LIVE -----# ... avoid long data accumulation by switching to "exactbars"
    elif args.historical is None:
    
        if args.ib is not None:
            cerebro.addstrategy(TheStrategy,
                                trade=args.trade,
                                exectype=bt.Order.ExecType(args.exectype),
                                stake=args.stake,
                                stopafter=args.stopafter,
                                valid=valid,
                                cancel=args.cancel,
                                donotsell=args.donotsell,
                                stoptrail=args.stoptrail,
                                stoptraillimit=args.traillimit,
                                trailamount=args.trailamount,
                                trailpercent=args.trailpercent,
                                limitoffset=args.limitoffset,
                                oca=args.oca,
                                bracket=args.bracket)
        elif args.oanda:
            cerebro.addstrategy(TheStrategy,
                                trade=args.trade,
                                exectype=bt.Order.ExecType(args.exectype),
                                stake=args.stake,
                                stopafter=args.stopafter,
                                valid=valid,
                                cancel=args.cancel,
                                donotcounter=args.donotcounter,
                                sell=args.sell,
                                usebracket=args.usebracket)
                                
        cerebro.addobserver(bt.observers.Trades)
        cerebro.addobserver(bt.observers.BuySell)
        cerebro.run(exactbars=args.exactbars)
        
#----- CEREBRO RUN - HISTORICAL -----#
    
    else:

        cerebro.addstrategy(TheStrategy)
        
        if args.writer:
            cerebro.addwriter(bt.WriterFile, csv=True, out=(args.data0 + "-from-" + args.fromdate + "-to-" + args.todate + ".csv"))
        cerebro.addobserver(bt.observers.Trades)
        cerebro.addobserver(bt.observers.BuySell)
        cerebro.addobserver(bt.observers.Value, barplot=True)
        cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='ta')
        cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')
        
        if data1 is not None:
            strategies = cerebro.run(stdstats=False, runonce=False) #runonce=False (for coupling of mixed tiemframes to work).
            firstStrat = strategies[0]
        else:
            strategies = cerebro.run(stdstats=False, runonce=True)
            firstStrat = strategies[0]
        
        #printstrategy()
        printTradeAnalysis(firstStrat.analyzers.ta.get_analysis())
        printSQN(firstStrat.analyzers.sqn.get_analysis())
        print('1.6 – 1.9 Below average, 2.0 – 2.4 Average, 2.5 – 2.9 Good, 3.0 – 5.0 Excellent, 5.1 – 6.9 Superb, 7.0 – Holy Grail')

        #portvalue = round(cerebro.broker.getvalue(), 2)
        portvalue = cerebro.broker.getvalue()
        #pnl = round(portvalue - startcash, 2)
        pnl = (portvalue - startcash)
        print('Final Portfolio Value: ${0:,.5f}'.format(portvalue))
        print('P/L: ${0:.5f}'.format(pnl))
        print('{0:.2f}%'.format((pnl / startcash * 100)))

        #pyplot.plot(data0) # TESTING DIFFERENT TYPE OF PLOTTING
        
        cerebro.plot(
            #plotter = None,
            #plotname=args.data0,
            #subplot=True,
            #plotlinelabels=True,
            style='candlestick',
            barup='green',
            bardown='red',
            volume=args.volume,
            )

if __name__ == '__main__':
    runstrategy()

[zkl]

Hello mics,

a few days ago I started to look into this subject.

This:

from matplotlib.finance import candlestick_ohlc

seems to be substituted through:

import mplfinance as mpf

mplfinance

matplotlib utilities for the visualization, and visual analysis, of financial data
https://github.com/matplotlib/mplfinance

[dasch]

backtrader_plotting is able to plot live data. I created a new project based on backtrader_plotting with live plotting in mind. You can find it here:

https://github.com/happydasch/btplotting

There are a few issues with generating/displaying data but it mostly works.

[dasch]

with btplotting you don't need a modified version of backtrader, you start it with this:

from btplotting import BacktraderPlottingLive
from btplotting.schemes import Tradimo

# black skin
cerebro.addanalyzer(BacktraderPlottingLive)

or

# white skin
cerebro.addanalyzer(BacktraderPlottingLive, scheme=Tradimo())

after this you can access the live plot at http://localhost:80

[rajanprabu]

@dasch This is wonderful, thank you so much. I just tested and it works just out of the box. Just one small comment. Please increase the default port number, otherwise one need supervisor credentials to make it run.

I have couple of questions. How does it handle resample data ? I just tried with ibstore and I can see the ticks but not resampled data.. any suggestions ? How does one disable the DEBUG aka logger messages ?

Once again thanks for sharing.

[dasch]

@rajanprabu said in Real-Time Plotting:

I have couple of questions. How does it handle resample data ? I just tried with ibstore and I can see the ticks but not resampled data.. any suggestions ? How does one disable the DEBUG aka logger messages ?

it should handle resampled data, but with ticks, the gaps are wide, depending on the resampled timeframe. for example, if you resample to 1 min from ticks, then they will not be that visible. In live plotting, you can configure what is being shown.

[dasch]

you can configure tick data to not be plotted. for the debugging messages, i am not sure what you mean.

[rajanprabu]

By DEBUG lines I meant these lines in the output

DEBUG:bokeh.server.tornado:[pid 72891] 0 clients connected
DEBUG:bokeh.server.tornado:[pid 72891]   / has 1 sessions with 1 unused
13-Oct-20 19:24:18 DEBUG     Sending patch for figurepage: defaultdict(<class 'list'>, {'datetime': [(0, Timestamp('2020-10-13 17:24:17.222988'))]})
13-Oct-20 19:24:18 DEBUG     Sending patch for figure: defaultdict(<class 'list'>, {'datetime': [(0, Timestamp('2020-10-13 17:24:17.222988'))]})
13-Oct-20 19:24:18 DEBUG     Sending patch for figure: defaultdict(<class 'list'>, {'140360762382800': [(0, 'NaN')], '140360762382992': [(0, 'NaN')], '140360762383184': [(0, 'NaN')], 'datetime': [(0, Timestamp('2020-10-13 17:24:17.222988'))]})
13-Oct-20 19:24:18 DEBUG     Sending patch for figure: defaultdict(<class 'list'>, {'140360474604560open': [(0, 'NaN')], '140360474604560high': [(0, 'NaN')], '140360474604560low': [(0, 'NaN')], '140360474604560close': [(0, 'NaN')], '140360474604560volume': [(0, 'NaN')], '140360762380368': [(0, 'NaN')], '140360762380560': [(0, 'NaN')], 'datetime': [(0, Timestamp('2020-10-13 17:24:17.222988'))]})
DEBUG:bokeh.server.contexts:Scheduling 1 sessions to discard
DEBUG:bokeh.server.contexts:Discarding session 'DsZV4Kgrj4zubFpXMK4MFTgQd3AyalCksvFAFmN5ItYq' last in use 26883.637406999987 milliseconds ago
DEBUG:bokeh.document.document:Deleting 0 modules for <bokeh.document.document.Document object at 0x7fa7e8e04c10>

@dasch said in Real-Time Plotting:

@rajanprabu said in Real-Time Plotting:

it should handle resampled data, but with ticks, the gaps are wide, depending on the resampled timeframe. for example, if you resample to 1 min from ticks, then they will not be that visible. In live plotting, you can configure what is being shown.

Understood, Thanks for your time and effort. Very much appreciate it.

[censorship]

Hello,

I'm exactly looking for something like this, i must say it looks surprising.
I merge historical data with livedata from a websocket to another dataframe.
This dataframe should then be displayed in a live chart.

However i wonder how to get started on this.
A little bit more information on how to use this would be very nice :D

Thanks in advance and thanks for your work