Plotting only chosen data?

Mango Loco

Hi there,

I work with a large universe (50+) and when backtesting, plotting the results is an unseeable mess.

I was wondering if it is possible to filter the results and to only plot certain data that meet the filter - a simple example would be the top 3 and bottom 3 performing assets in the backtest.

Any help would be much appreciated!

run-out

What to you mean by top 3 and bottom 3 performing? Could you be more specific?

run-out

Perhaps this can help you in the meantime. If you know which stocks you want to chart, you can add the following after you run cerebro and before you run cerebro.plot(). Below are some sample stocks in a list.

wins_losses = ['CMD', 'EW', 'BKNG' ]
    for d in cerebro.datas:
        if d._name in wins_losses:
            d.plotinfo.plot = True

You must tell backtrader not to plot the lines when you are adding in your data lines.

You do this by setting 'plot=False' when creating your dataline.

data = bt.feeds.PandasData(dataname=dataframe, name='AMZN', plot=False)

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

backtrader

@run-out said in Plotting only chosen data?:

You do this by setting 'plot=False' when creating your dataline.

This can be done at anytime, not only during creation.

Mango Loco

@run-out
Thanks for your input! Unfortunately, I don't know ahead of time which assets I would like to plot.

I would like to access something rank-able, such as returns of each asset, and make plotting decisions based on that (top-performing, mean, bottom-performing - for example).

Any ideas or tips on achieving that?

Mango Loco

@backtrader
Sorry to bring up an old thread, but I'm still having issues with one aspect of the above.

I would like to access the returns information of each data within the strategy. I don't think this is currently possible - could you help me with this?

Thanks in advance!

backtrader

Data feeds per se have no return. Pseudo-Return (i.e.: the variation) can be calculated using Analyzers.

https://www.backtrader.com/blog/posts/2016-07-22-benchmarking/benchmarking/

Aritz Lizaso

Hi, how can i not plot the resample data? I can change this option for normal datas and it works but i cant hide resampled data of my pivotpoints indicator.

backtrader

Let us see what you do ... to find out why you cannot.

Aritz Lizaso

'''
if name == 'main':

# Instantiate the Cerebro engine  
cerebro = bt.Cerebro()  

# Add a strategy
cerebro.addstrategy(PIVOTPOINTS_V8)

# Add the Data Feeds to Cerebro
cerebro.adddata(data_eurusd_m1)
cerebro.adddata(data_eurusd_m30)
cerebro.adddata(data_eurusd_d1)
#cerebro.adddata(data_gbpusd_m1)
#cerebro.adddata(data_gbpusd_m30)
#cerebro.adddata(data_gbpusd_d1)
#cerebro.adddata(data_usdjpy_m1)
#cerebro.adddata(data_usdjpy_m30)
#cerebro.adddata(data_usdjpy_d1)

# Resampled data for the indicator
cerebro.resampledata(data_eurusd_m1, timeframe=bt.TimeFrame.Days)

# Set our desired cash start
cerebro.broker.setcash(100000.0)

cerebro.broker.set_checksubmit(False)

# Set the commission - 25 monetary units per 1 lot (1 lot = 100.000 monetary units), per buy/sell = 0.0125%, total= 0.025% 
cerebro.broker.setcommission(commission=0.000125)

# Add Analyzers

#cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='TradeAnalyzer')
#cerebro.addanalyzer(bt.analyzers.Returns, _name='Returns')
#cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='TimeReturn')
#cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions')
#cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='PositionsValue')
#cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')
#cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn')

# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) 

# Run over everything (loop over the data)
results = cerebro.run() 
strat = results[0]

# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) 

cerebro.plot(style='candle', numfigs=1, barup ='lime', bardown ='red', valuetags=False)

'''
backtrader plot.png

backtrader

FIRST LESSON IN ALGOTRADING (believe me when I say it is an important one)

In algotrading like in chess, look carefully at what's right before your eyes, like what's at the top of every page of this community.

For code/output blocks: Use ``` (aka backtick or grave accent) in a single line before and after the block. See: http://commonmark.org/help/

2nd, this thread says (one example)

@backtrader said in Plotting only chosen data?:

@run-out said in Plotting only chosen data?:

You do this by setting plot=False when creating your dataline.

This can be done at anytime, not only during creation.

The same can be read in the documentation here: https://www.backtrader.com/docu/plotting/plotting/

Where are you doing that in your code which seems fairly incomplete? (Actually for any of the data, you have many and only 2 are shown)

You are not even storing the resampled data instance in a variable to do that

Nobody is interested in your secret sauce. If you want to show something and hide your very profitable code, create a 10 line snippet to show the problem.

Aritz Lizaso

Thank you for your answer,

The first thing I apologize, I think I did not know how to put the code well.

The second my code is not secret, its only a test. I put only the backtest running parts because there are too many lines.

I read the documentation before post and had no problem hiding the imported data directly. My problem is that I don't know how to hide daily resampled data in the chart. Perhaps i lose something but I really can't find anything about it. Here I show everything:

from __future__ import (absolute_import, division, print_function, unicode_literals)

import backtrader as bt  # The backtrader platform
import backtrader.feeds as btfeeds  # For load CSV data.
from backtrader import Indicator, CmpEx # For indicators
import backtrader.indicators as btind  # For indicators

import datetime  # For datetime objects
from datetime import time
import matplotlib # For ploting

# Symbols to load
symbol_list = ['EURUSD','GBPUSD','USDJPY']

# List for storage the loaded datas
data_list = []

# Load the data feeds
for symbol in symbol_list:     
    
    # 1 minute data (Each tick for analyze the strategy)_______________________________________________________
    data0 = btfeeds.GenericCSVData(
    dataname='{}_GMT+0_2018-2019_M1.csv'.format(symbol),    

    fromdate=datetime.datetime(2018, 1, 1),
    todate=datetime.datetime(2018, 1, 31),  #(2018, 1, 31)

    nullvalue=0.0,

    dtformat=('%d.%m.%Y %H:%M:%S'),

    datetime=0,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=-1,
    openinterest=-1,

    timeframe=bt.TimeFrame.Minutes,
    compression=1,
        
    plot = True
    ) 
   
    data_list.append(data0)
    
    # 30 minute data (Pullback checking and plotting)__________________________________________________________
    data1 = btfeeds.GenericCSVData(
    dataname='{}_GMT+0_2018-2019_M30.csv'.format(symbol),    

    fromdate=datetime.datetime(2018, 1, 1),
    todate=datetime.datetime(2018, 1, 31),

    nullvalue=0.0,

    dtformat=('%d.%m.%Y %H:%M:%S'),

    datetime=0,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=-1,
    openinterest=-1,

    timeframe=bt.TimeFrame.Minutes,
    compression=30,
        
    plot = False
    ) 
   
    data_list.append(data1)
    
    # 1 Day data (For pivot points indicator)_______________________________________________________________
    data2 = btfeeds.GenericCSVData(
    dataname='{}_GMT+0_2018-2019_D1.csv'.format(symbol),    

    fromdate=datetime.datetime(2018, 1, 1),
    todate=datetime.datetime(2018, 1, 31),

    nullvalue=0.0,

    dtformat=('%d.%m.%Y %H:%M:%S'),

    datetime=0,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=-1,
    openinterest=-1,

    timeframe=bt.TimeFrame.Days,
    compression=1,
        
    plot = False
    ) 
   
    data_list.append(data2)
       
# Assign data feed variables
data_eurusd_m1 = data_list[0]
data_eurusd_m30 = data_list[1]
data_eurusd_d1 = data_list[2]
data_gbpusd_m1 = data_list[3]
data_gbpusd_m30 = data_list[4]
data_gbpusd_d1 = data_list[5]
data_usdjpy_m1 = data_list[6]
data_usdjpy_m30 = data_list[7]
data_usdjpy_d1 = data_list[8]

class PivotPoints_V8_Indicator(Indicator):
   
    lines = ('PP', 'R1', 'S1', 'R2', 'S2', 'R3', 'S3')
    #plotinfo = dict(subplot=False)
    plotinfo = dict(subplot=False)
    
    params = (('_autoplot', True),)  # attempt to plot on real target data

    def _plotinit(self):
        
        # Try to plot to the actual timeframe master
        if self.p._autoplot:
            if hasattr(self.data, 'data'):
                self.plotinfo.plotmaster = self.data.data
                #self.plotinfo.plotmaster = self.data.datas[1] # The second Data (M30) 
                # The indicator plots on resampled data*

    def __init__(self):
        
        h = self.data.high  # current high
        l = self.data.low  # current low
        c = self.data.close  # current close

      
        self.lines.PP = PP = (h + l + c) / 3.0
        self.lines.R1 = R1 = 2.0 * PP - l
        self.lines.S1 = S1 = 2.0 * PP - h
        self.lines.R2 = R2 = (PP - S1) + R1
        self.lines.S2 = S2 = PP - (R1 - S1)
        self.lines.R3 = (PP - S2) + R2
        self.lines.S3 = PP - (R2 - S2)

        #super(PivotPoint, self).__init__()  # enable coopertive inheritance

        if self.p._autoplot:
            self.plotinfo.plot = False  # disable own plotting
            self()  # Coupler to follow real object


def time_in_range(start, end, x):
    
    """Return true if x is in the range [start, end]"""
    
    if start <= end:
        return start <= x <= end
    else:
        return start <= x or x <= end

# The Strategy

class PIVOTPOINTS_V8(bt.Strategy):
    
    
    # datas[0] = EURUSD
    #...
    # datas[3] = GBPUSD
    #...
    # datas[6] = USDJPY
    #...
    
    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.datetime(0)
        print('%s, %s' % (dt.strftime('%d-%m-%Y %H:%M:%S'), txt))

    def __init__(self):
    
        # Strategy symbol
        self.symbol = 'EURUSD.r' #,'GBPUSD.r','USDJPY.r'
        
        # Indicator
        self.pivotindicator = PivotPoints_V8_Indicator(self.datas[3])  # the resampled data
        #self.pivotindicator.plotinfo.plotlinelabels = True
        
        # Data references
        self.M1_datetime = self.datas[0].datetime
        self.M1_close = self.datas[0].close
        self.M30_close = self.datas[1].close
        self.D1_close = self.datas[2].close
        self.D1_high = self.datas[2].high
        self.D1_low = self.datas[2].low
        
        # Strategy variables
        self.pullback_size = 70
        self.pullback_candles = 10 #(M30 Candles!)
        self.op_counters = [0,0,0,0,0,0,0,0,0,0,0,0,0,0]
        self.send_distance = 70  #(Points)
        self.open_distance = 20  #(Points)
        self.lot = 1.5  #(1 lot = 100.000 unitary units)
        self.stoploss = 100
        self.takeprofit = 100
        
        # Keep a reference to the orders   
        #self.order = None
        
    def notify_order(self, order):
        #date = self.data.datetime.datetime().date()
        date = self.data.datetime.datetime()

        if order.status == order.Accepted:
            print('-'*32,' NOTIFY ORDER ','-'*32)
            print('Order Accepted')
            print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format(
                                                        date,
                                                        order.status,
                                                        order.ref,
                                                        order.size,
                                                        'NA' if not order.price else round(order.price,5)
                                                        ))


        if order.status == order.Completed:
            print('-'*32,' NOTIFY ORDER ','-'*32)
            print('Order Completed')
            print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format(
                                                        date,
                                                        order.status,
                                                        order.ref,
                                                        order.size,
                                                        'NA' if not order.price else round(order.price,5)
                                                        ))
            print('Created: {} Price: {} Size: {}'.format(bt.num2date(order.created.dt), order.created.price,order.created.size))
            print('-'*80)

        if order.status == order.Canceled:
            print('-'*32,' NOTIFY ORDER ','-'*32)
            print('Order Canceled')
            print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format(
                                                        date,
                                                        order.status,
                                                        order.ref,
                                                        order.size,
                                                        'NA' if not order.price else round(order.price,5)
                                                        ))

        if order.status == order.Rejected:
            print('-'*32,' NOTIFY ORDER ','-'*32)
            print('WARNING! Order Rejected')
            print('{}, Status {}: Ref: {}, Size: {}, Price: {}'.format(
                                                        date,
                                                        order.status,
                                                        order.ref,
                                                        order.size,
                                                        'NA' if not order.price else round(order.price,5)
                                                        ))
            print('-'*80)


    def notify_trade(self, trade):
        date = self.data.datetime.datetime()
        if trade.isclosed:
            print('-'*32,' NOTIFY TRADE ','-'*32)
            print('{}, Close Price: {}, Profit, Gross {}, Net {}'.format(
                                                date,
                                                trade.price,
                                                round(trade.pnl,2),
                                                round(trade.pnlcomm,2)))
            print('-'*80)

    def next(self):
        
        #//////// Trading Time //////////////////////////////////////////////////////////////
        
        start = time(4,0,0) #4,0,0
        end = time (20,50,0) #20,50,0
        now = self.M1_datetime.time()
        weekday = self.datas[0].datetime.datetime(0).weekday()
        
        if time_in_range(start, end, now) and weekday in [1,2,3,4]:  #(Monday=0, Tueasday=1...Sunday=6)

            # --- Get Data ----------------------------------------------------------------------

            # Pivot Points values

            PP = self.pivotindicator.lines.PP[0]
            R1 = self.pivotindicator.lines.R1[0]
            S1 = self.pivotindicator.lines.S1[0]
            R2 = self.pivotindicator.lines.R2[0]
            S2 = self.pivotindicator.lines.S2[0]
            R3 = self.pivotindicator.lines.R3[0]
            S3 = self.pivotindicator.lines.S3[0]

            # List of pivot point levels for send orders iteration (Buy and Sell each level)
            pp_levels = [PP,PP,R1,R1,S1,S1,R2,R2,S2,S2,R3,R3,S3,S3]

            # Price (M1 Close, Backtrader cant handle tick data for Bid and Ask prices)
            price = self.M1_close[0]

            # Point value
            if 'JPY' in self.symbol:
                point = 0.001
            else:
                point = 0.00001

            # Pullbacks 
            periods_back = self.pullback_candles  #(M30 Candles)
            back_rates = self.M1_close.get(size=periods_back, ago=0)

            highest = max(back_rates)
            lowest = min(back_rates)

            pullback_buy = (highest - price) / point > self.pullback_size 
            pullback_sell = (price - lowest) / point > self.pullback_size

            # --- Send Orders ---------------------------------------------------------------

            # Check if any entry condition has been met
            for i in range(len(self.op_counters)):

                # BUY iterations on pivot levels
                if i in [0,2,4,6,8,10,12]:

                    if price > pp_levels[i] and price < (pp_levels[i] + self.send_distance * point) and pullback_buy == True and self.op_counters[i] == 0 :  

                        price = pp_levels[i]+(self.open_distance*point) # Price of the limit order
                        sl = price - (self.stoploss*point)
                        tp =price + (self.takeprofit*point)

                        buy_order = self.buy_bracket(limitprice=tp, price=price, stopprice=sl, exectype=bt.Order.Limit, size= self.lot*100000)

                        # Update ticket

                        # Update counter
                        self.op_counters[i] +=1

                # SELL iterations on pivot levels
                elif i in [1,3,5,7,9,11,13]:

                    if price < pp_levels[i] and price > (pp_levels[i] - self.send_distance * point) and pullback_sell == True and self.op_counters[i] == 0 :  

                        price = pp_levels[i]-(self.open_distance*point) # Price of the limit order
                        sl = price + (self.stoploss*point)
                        tp =price - (self.takeprofit*point)

                        sell_order = self.sell_bracket(limitprice=tp, price=price, stopprice=sl, exectype=bt.Order.Limit, size= self.lot*100000)

                        # Update ticket

                        # Update counter
                        self.op_counters[i] +=1

            # --- Orders Managment -----------------------------------------------------------
            
            


        
        else:
            
            #//////// Out of trading Time ////////////////////////////////////////////////////
              
            # --- Reset ----------------------------------------------------------------------
            
            self.op_counters = [0,0,0,0,0,0,0,0,0,0,0,0,0,0]
            
            # --- Close all ----------------------------------------------------------------------
            
            if self.position:
                self.close()
                #self.close(data= self.datas[0])


if __name__ == '__main__':
    
    # Instantiate the Cerebro engine  
    cerebro = bt.Cerebro(stdstats=False)  # remove the standard observers
    
    # Add a strategy
    cerebro.addstrategy(PIVOTPOINTS_V8)
    
    # Add the Data Feeds to Cerebro
    cerebro.adddata(data_eurusd_m1)
    cerebro.adddata(data_eurusd_m30)
    cerebro.adddata(data_eurusd_d1)
    #cerebro.adddata(data_gbpusd_m1)
    #cerebro.adddata(data_gbpusd_m30)
    #cerebro.adddata(data_gbpusd_d1)
    #cerebro.adddata(data_usdjpy_m1)
    #cerebro.adddata(data_usdjpy_m30)
    #cerebro.adddata(data_usdjpy_d1)
    
    # Resampled data for the indicator
    cerebro.resampledata(data_eurusd_m1, timeframe=bt.TimeFrame.Days)
    
    # Set our desired cash start
    cerebro.broker.setcash(100000.0)
    
    cerebro.broker.set_checksubmit(False)
    
    # Set the commission - 25 monetary units per 1 lot (1 lot = 100.000 monetary units), per buy/sell = 0.0125%, total= 0.025% 
    cerebro.broker.setcommission(commission=0.000125)
    
    # Add Analyzers
    
    #cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='TradeAnalyzer')
    #cerebro.addanalyzer(bt.analyzers.Returns, _name='Returns')
    #cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='TimeReturn')
    #cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions')
    #cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='PositionsValue')
    #cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')
    #cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn')
    
    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) 
    
    # Run over everything (loop over the data)
    results = cerebro.run() 
    strat = results[0]
    
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) 


    cerebro.plot(style='candle', numfigs=1, barup ='lime', bardown ='red', valuetags=False)