Backtrader - Portfolio Rebalancing with Alpha Vantage

Warning: Declaration of ElementorPro\Modules\Posts\Skins\Skin_Content_Base::register_controls(Elementor\Widget_Base $widget) should be compatible with Elementor\Controls_Stack::register_controls() in /home/customer/www/backtest-rookies.com/public_html/wp-content/plugins/elementor-pro/modules/theme-builder/widgets/post-content.php on line 17

In this post, we will take a look at how to perform a simple 60/40 portfolio rebalancing strategy in Backtrader. For those of you that are unaware, the 60/40 portfolio is almost like the “hello world” of portfolios. It describes the ratio of stocks to bonds in the portfolio. In other words, a ratio of 60% stocks and 40% bonds. More on that later…

Diversification and Portfolio Rebalancing

The old saying goes, “don’t put all of your eggs in one basket”. That is often good advice for investing. In our world, we can think of the eggs are our selected investments and asset classes are our baskets. If one basket breaks (e.g. stocks) then (hopefully) another basket will be stronger (e.g. bonds). So the idea is to select a mixture of non-correlated assets. (assets which do not go up and down at the same time!).

Rebalancing a portfolio is the simple exercise of making sure you don’t fill one basket with too many eggs. At regular intervals, we take a look at our portfolio “weight” and decide whether we need to reduce or increase the size of each asset in order to balance the baskets.

The 60/40

The general idea behind selecting a ratio of 60% stocks and 40% bonds is that traditionally, they have been inversely correlated. This means that when the stock market cools off, bond prices tend to rise and act as a hedge against losing your shirt. The portfolio is weighted towards stocks as they tend to appreciate more over the long run.

Of course, nothing is perfect and we are starting to see some commentators speculate that the inverse correlation between stocks and bonds is ready to die. As such, readers should not treat this overview as an endorsement or basis for an investment decision.

Requirements

This article builds on the shoulders of some other posts which use Alpha Vantage as a data source. As such, this article will not cover the mechanics of downloading and ingesting the data. To learn more about how this is done, please take a look at these articles.

API Key

If you have not read the articles above, you will need to get yourself an API key or swap out the data feeds to use this code.

The API key will grant you lifetime access to Alpha Vantage data. Head over to the following link and sign up.

Source: https://www.alphavantage.co/support/#api-key

Adjusted Data

This post will make use of adjusted price data which will allow us to easily simulate dividend re-investment. For more information regarding dividends and adjusted data, see these articles:

Example Code

'''
Author: www.backtest-rookies.com

MIT License

Copyright (c) 2020 backtest-rookies.com

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.
'''
import backtrader as bt
from datetime import datetime
import pandas as pd
import numpy as np
from alpha_vantage.timeseries import TimeSeries

# IMPORTANT!
# ----------
# Register for an API at:
# https://www.alphavantage.co/support/#api-key
# Then insert it here.
Apikey = 'INSERT YOUR API KEY'


def adjust(date, close, adj_close, in_col, rounding=4):
    '''
    If using forex or Crypto - Change the rounding accordingly!
    '''
    try:
        factor = adj_close / close
        return round(in_col * factor, rounding)
    except ZeroDivisionError:
        print('WARNING: DIRTY DATA >> {} Close: {} | Adj Close {} | in_col: {}'.format(date, close, adj_close, in_col))
        return 0


def alpha_vantage_daily_adjusted(symbol_list, compact=False, debug=False, rounding=4, *args, **kwargs):
    '''
    Helper function to download Alpha Vantage Data.

    My framework expects a nested list to be returned containing the
    pandas dataframe and the name of the feed.
    '''
    data_list = list()

    size = 'compact' if compact else 'full'

    for symbol in symbol_list:

        if debug:
            print('Downloading: {}, Size: {}'.format(symbol, size))

        # Submit our API and create a session
        alpha_ts = TimeSeries(key=Apikey, output_format='pandas')

        # Get the data
        data, meta_data = alpha_ts.get_daily_adjusted(symbol=symbol, outputsize=size)
        #data, meta_data = alpha_ts.get_daily(symbol=symbol, outputsize=size)

        if debug:
            print(data)

        #Convert the index to datetime.
        data.index = pd.to_datetime(data.index)
        # Adjust the rest of the data
        data['adj open'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['1. open'], rounding=rounding)
        data['adj high'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['2. high'], rounding=rounding)
        data['adj low'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['3. low'], rounding=rounding)

        # Extract the colums we want to work with and rename them.
        data = data[['adj open', 'adj high', 'adj low','5. adjusted close','6. volume']]
        data.columns = ['Open','High','Low','Close','Volume']

        data_list.append((data, symbol))

    return data_list

class RebalanceStrategy(bt.Strategy):

    params = (('assets', list()),
        ('rebalance_months', [1,6]),) # Float: 1 == 100%

    def __init__(self):
        self.rebalance_dict = dict()
        for i, d in enumerate(self.datas):
            self.rebalance_dict[d] = dict()
            self.rebalance_dict[d]['rebalanced'] = False
            for asset in self.p.assets:
                if asset[0] == d._name:
                    self.rebalance_dict[d]['target_percent'] = asset[1]

    def next(self):

        for i, d in enumerate(self.datas):
            dt = d.datetime.datetime()
            dn = d._name
            pos = self.getposition(d).size

            if dt.month in self.p.rebalance_months and self.rebalance_dict[d]['rebalanced'] == False:
                print('{} Sending Order: {} | Month {} | Rebalanced: {} | Pos: {}'.format(dt,dn,dt.month, self.rebalance_dict[d]['rebalanced'], pos ))
                self.order_target_percent(d, target=self.rebalance_dict[d]['target_percent']/100)
                self.rebalance_dict[d]['rebalanced'] = True

            # Reset
            if dt.month not in self.p.rebalance_months:
                self.rebalance_dict[d]['rebalanced'] = False


    def notify_order(self, order):
        date = self.data.datetime.datetime().date()

        if order.status == order.Completed:
            print('{} >> Order Completed >> Stock: {},  Ref: {}, Size: {}, Price: {}'.format(

                                                        date,
                                                        order.data._name,
                                                        order.ref,
                                                        order.size,
                                                        'NA' if not order.price else round(order.price,5)
                                                        ))



    def notify_trade(self, trade):
        date = self.data.datetime.datetime().date()
        if trade.isclosed:
            print('{} >> Notify Trade >> Stock: {}, Close Price: {}, Profit, Gross {}, Net {}'.format(
                                                date,
                                                trade.data._name,
                                                trade.price,
                                                round(trade.pnl,2),
                                                round(trade.pnlcomm,2)))

startcash = 10000

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

# strategy Params
strat_params = [
    ('VUKE.L', 30),
    ('VUSA.L', 30),
    ('VGOV.L', 20),
    ('INXG.L', 20)

]

symbol_list = [x[0] for x in strat_params]

#Add our strategy
cerebro.addstrategy(RebalanceStrategy, assets=strat_params)


data_list = alpha_vantage_daily_adjusted(
                symbol_list,
                compact=False,
                debug=False)

for i in range(len(data_list)):

    data = bt.feeds.PandasData(
                dataname=data_list[i][0], # This is the Pandas DataFrame
                name=data_list[i][1], # This is the symbol
                timeframe=bt.TimeFrame.Days,
                compression=1,
                fromdate=datetime(2014,1,1),
                todate=datetime(2020,1,1)
                )

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


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

# 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')

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

'''

Author: www.backtest-rookies.com

MIT License

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.

'''

import backtrader as bt

from datetime import datetime

import pandas as pd

import numpy as np

from alpha_vantage.timeseries import TimeSeries

# IMPORTANT!

# ----------

# Register for an API at:

# https://www.alphavantage.co/support/#api-key

# Then insert it here.

Apikey = 'INSERT YOUR API KEY'

def adjust(date, close, adj_close, in_col, rounding=4):

'''

If using forex or Crypto - Change the rounding accordingly!

'''

try:

factor = adj_close / close

return round(in_col * factor, rounding)

except ZeroDivisionError:

print('WARNING: DIRTY DATA >> {} Close: {} | Adj Close {} | in_col: {}'.format(date, close, adj_close, in_col))

return 0

def alpha_vantage_daily_adjusted(symbol_list, compact=False, debug=False, rounding=4, *args, **kwargs):

'''

Helper function to download Alpha Vantage Data.

My framework expects a nested list to be returned containing the

pandas dataframe and the name of the feed.

'''

data_list = list()

size = 'compact' if compact else 'full'

for symbol in symbol_list:

if debug:

print('Downloading: {}, Size: {}'.format(symbol, size))

# Submit our API and create a session

alpha_ts = TimeSeries(key=Apikey, output_format='pandas')

# Get the data

data, meta_data = alpha_ts.get_daily_adjusted(symbol=symbol, outputsize=size)

#data, meta_data = alpha_ts.get_daily(symbol=symbol, outputsize=size)

if debug:

print(data)

#Convert the index to datetime.

data.index = pd.to_datetime(data.index)

# Adjust the rest of the data

data['adj open'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['1. open'], rounding=rounding)

data['adj high'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['2. high'], rounding=rounding)

data['adj low'] = np.vectorize(adjust)(data.index.date, data['4. close'], data['5. adjusted close'], data['3. low'], rounding=rounding)

# Extract the colums we want to work with and rename them.

data = data[['adj open', 'adj high', 'adj low','5. adjusted close','6. volume']]

data.columns = ['Open','High','Low','Close','Volume']

data_list.append((data, symbol))

return data_list

class RebalanceStrategy(bt.Strategy):

params = (('assets', list()),

('rebalance_months', [1,6]),) # Float: 1 == 100%

def __init__(self):

self.rebalance_dict = dict()

for i, d in enumerate(self.datas):

self.rebalance_dict[d] = dict()

self.rebalance_dict[d]['rebalanced'] = False

for asset in self.p.assets:

if asset[0] == d._name:

self.rebalance_dict[d]['target_percent'] = asset[1]

def next(self):

for i, d in enumerate(self.datas):

dt = d.datetime.datetime()

dn = d._name

pos = self.getposition(d).size

if dt.month in self.p.rebalance_months and self.rebalance_dict[d]['rebalanced'] == False:

print('{} Sending Order: {} | Month {} | Rebalanced: {} | Pos: {}'.format(dt,dn,dt.month, self.rebalance_dict[d]['rebalanced'], pos ))

self.order_target_percent(d, target=self.rebalance_dict[d]['target_percent']/100)

self.rebalance_dict[d]['rebalanced'] = True

# Reset

if dt.month not in self.p.rebalance_months:

self.rebalance_dict[d]['rebalanced'] = False

def notify_order(self, order):

date = self.data.datetime.datetime().date()

if order.status == order.Completed:

print('{} >> Order Completed >> Stock: {}, Ref: {}, Size: {}, Price: {}'.format(

date,

order.data._name,

order.ref,

order.size,

'NA' if not order.price else round(order.price,5)

))

def notify_trade(self, trade):

date = self.data.datetime.datetime().date()

if trade.isclosed:

print('{} >> Notify Trade >> Stock: {}, Close Price: {}, Profit, Gross {}, Net {}'.format(

date,

trade.data._name,

trade.price,

round(trade.pnl,2),

round(trade.pnlcomm,2)))

startcash = 10000

#Create an instance of cerebro

cerebro = bt.Cerebro()

# strategy Params

strat_params = [

('VUKE.L', 30),

('VUSA.L', 30),

('VGOV.L', 20),

('INXG.L', 20)

]

symbol_list = [x[0] for x in strat_params]

#Add our strategy

cerebro.addstrategy(RebalanceStrategy, assets=strat_params)

data_list = alpha_vantage_daily_adjusted(

symbol_list,

compact=False,

debug=False)

for i in range(len(data_list)):

data = bt.feeds.PandasData(

dataname=data_list[i][0], # This is the Pandas DataFrame

name=data_list[i][1], # This is the symbol

timeframe=bt.TimeFrame.Days,

compression=1,

fromdate=datetime(2014,1,1),

todate=datetime(2020,1,1)

)

#Add the data to Cerebro

cerebro.adddata(data)

# Set our desired cash start

cerebro.broker.setcash(startcash)

cerebro.broker.set_checksubmit(False)

# 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')

Code Commentary

The strategy itself is a relatively small part of the overall code but we will mainly focus on it as the data download and general setup of Backtrader is covered in other articles. The strategy will rebalance each asset on the first call of next() when the month is equal to the given “rebalance” months. The rebalance months can be adjusted as strategy parameters with 1 (Jan) and 6 (Jun) as the defaults.

checksubmit

The one part of the setup that is worth mentioning is checksubmit. We set this parameter to False which means that orders will not be checked to see if you can afford it before submitting them. We need that as we might be sending orders to increase the weight of an asset before decreasing another. More on that later…

init()

During init()we simply loop through each of the data feeds and assign the given ideal weights to each. We also add a simple boolean flag that we will use for tracking whether we have rebalanced each asset. Matching of the weights to each asset is done by comparing the data feed name to name in the asset list that is passed to the strategy on startup.

# strategy Params
strat_params = [
    ('VUKE.L', 30),
    ('VUSA.L', 30),
    ('VGOV.L', 20),
    ('INXG.L', 20)

]

symbol_list = [x[0] for x in strat_params]

#Add our strategy
cerebro.addstrategy(RebalanceStrategy, assets=strat_params)

# strategy Params

strat_params = [

('VUKE.L', 30),

('VUSA.L', 30),

('VGOV.L', 20),

('INXG.L', 20)

]

symbol_list = [x[0] for x in strat_params]

#Add our strategy

cerebro.addstrategy(RebalanceStrategy, assets=strat_params)

next()

During next() we loop through each datafeed and check whether the most recent datetimeis a match for our target rebalance months. If it is, we send an order to rebalance and then update our rebalance_dict so we don’t try and rebalance again this month.

Calculating how much we need to buy or sell to rebalance is easy. We can simply let Backtrader handle it with the built-in order_target_percent()function. This is super handy for rebalancing. It will perform the calculations you need and either buy or sell an asset to get you to the target weight. This is also why the checksubmit argument is needed that we discussed earlier. Due to the way we loop through all the data feeds, there is no guarantee that the first asset we loop through is an asset we want to sell before increasing the weight of another. For this reason, we want to be able to send an order to increase the weight of an asset before looping through to the next asset.

Finally, once the current month is not equal to one of our rebalance months, we can safely update the rebalance_dictand set the asset to Falseagain for rebalancing.

Results

Running the code, you should see something like this on the charts. Note that not all assets will be bought or sold during each rebalance cycle. They may already be close to the target weight and buying or selling would take you further from it. In these cases, no operation needs to be performed.

Find This Post Useful?

If this post saved you time and effort, please consider support the site! There are many ways to support us and some won’t even cost you a penny.

Brave

Tradingview

PayPal

BTC

ETH

LTC

Backtrader – Portfolio Rebalancing with Alpha Vantage

Diversification and Portfolio Rebalancing

The 60/40

Requirements

API Key

Adjusted Data

Example Code

Code Commentary

checksubmit

init()

next()

Results

Find This Post Useful?

Related

One Response

Leave a Reply

Diversification and Portfolio Rebalancing

The 60/40

Requirements

API Key

Adjusted Data

Example Code

Code Commentary

checksubmit

init()

next()

Results

Find This Post Useful?

Share this:

Related

One Response

Leave a Reply