pyportfoliotracker 0.1.0

Track the performance of your portfolio of equities vs an index

portfolio-tracker

Track an equity portfolio's performance against a major index.

Refer to Sample Code at the bottom for a template on how to use this package.

0. Initialising

The following packages are required: yahoofinancials, pandas, matplotlib, datetime, numpy

Enter the following imports at the top of the file:

import pyportfoliotracker
from pyportfoliotracker import Fund

1. Setting up your fund

Set up your fund using the following parameters:

fund = Fund(cash, index_ticker, date_of_creation, strategy, risk_free_rate_percentage):

where

• cash = cash value of the fund
• index_ticker = the ticker (based on Yahoo Finance) of the benchmark index
• date_of_creation = date of creation of the fund
• strategy (optional) = strategy used for the index benchmark. The default strategy is lump_sum. The other option is dca10, which represents Dollar Cost Averaging using 10% of cash value per day.
• risk_free_rate_percentage (optional) = the risk free rates, in percentage (e.g. enter 2.5 for 2.5%), that will be used to calculate alpha, beta and Sharpe ratio. The default is set to 2.5%.

2. Purchasing/Selling the relevant equities

Buying

Using the .buy_equity() method, purchase the relevant equities that are present in your fund.

fund.buy_equity(ticker, date_of_purchase, qty, price)

where

• ticker = the ticker (based on Yahoo Finance) of the equity
• date_of_purchase = date of purchase of the equity
• qty = quantity of equity purchased
• price = price at which equity was purchased

Selling

Using the .sell_equity() method, purchase the relevant equities that are present in your fund.

fund.sell_equity(ticker, date_of_sale, qty, price)

where

• ticker = the ticker (based on Yahoo Finance) of the equity
• date_of_sale = date of sale of the equity
• qty = quantity of equity purchased
• price = price at which equity was purchased

3. Selecting your desired output

There are many types of output that can be useful to you:

1a. DataFrame of the fund's historical paper value vs Index : obtained by calling print(fund.all_assets_normalised)

Note that simply calling the attribute fund.all_assets_normalised returns you the DataFrame that can be integrated into other packages and use cases.

1b. Exporting the DataFrame mentioned in 1a into a CSV : obtained by calling fund.export_to_csv(output_path)

Note that the variable output_path in .export_to_csv is set to 'data/historical-paper-values.csv' by default.

2a. Graphical comparison of fund vs index performance : obtained by calling fund.plot_fund_performance()

2b. Exporting the Graph mentioned in 2a into a PNG : obtained by calling fund.export_graph(output_path)

Note that the variable output_path in .export_graph is set to 'data/fund-graph-plot.png' by default.

3a. DataFrame of the fund's key financial metrics e.g. alpha, beta, Sharpe's Ratio : obtained by calling print(fund.fund_metrics_table())

Note that simply calling the method fund.fund_metrics_table() returns you the DataFrame that can be integrated into other packages and use cases.

3b. Exporting the DataFrame mentioned in 3a into a CSV : obtained by calling fund.export_fund_metrics(output_path)

Note that the variable output_path in .export_fund_metrics is set to 'data/fund-metrics.csv' by default.

Sample Code:

"""
Imports
"""
import pyportfoliotracker
from pyportfoliotracker import Fund

"""
Set up your fund
"""
date = '2020-05-18'
fund = Fund(2375706,'^FTSE',date,strategy='dca10', risk_free_rate_percentage=0.65)

"""
Buy the relevant equities
"""
fund.buy_equity('GSK.L',date,397,1670.20)
fund.buy_equity('SGE.L',date,802,653)
fund.buy_equity('NG.L',date,394,922.2)
fund.buy_equity('WHR.L',date,3345,100.5)
fund.buy_equity('SSE.L',date,161,1242.50)
fund.buy_equity('RHIM.L',date,69,2306.00)
fund.buy_equity('ICP.L',date,64,1109.00)
fund.buy_equity('ASC.L',date,21,2768.8)

"""
Sell the relevant equities
"""
fund.sell_equity('GSK.L','2020-05-22',397,1670.20)

"""
Call methods based on output desired
"""
print(fund.all_assets_normalised.head()) #Returns a DataFrame of the historical performance of the fund
fund.plot_fund_performance() #Returns a graphical plot of the fund vs index
print(fund.fund_metrics_table()) #Returns a DataFrame of key fund metrics (alpha, beta, Sharpe's ratio)
fund.export_to_csv() #Exports the DataFrame of historical performance into a CSV
fund.export_graph() #Exports the graphical plot of the fund vs index into a PNG
fund.export_fund_metrics() #Exports the DataFrame of key fund metrics into a CSV