Metadata-Version: 2.1
Name: pyportfoliotracker
Version: 0.0.5
Summary: Track the performance of your portfolio of equities vs an index
Home-page: https://github.com/bryan-teng/portfolio-tracker
Author: Bryan Teng
Author-email: teng.weiyan.bryan@gmail.com
License: UNKNOWN
Description: # 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`
        
        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):`
        
        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.
        
        ## 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 2 types of output that can be obtained:
        
        **1. Graphical comparison of fund vs index performance** : obtained by calling `fund.plot_fund_performance()`
        
        <img src="/src/graphical_output.png" alt="Graphical Output of Fund Performance"/>
        
        **2. CSV file containing the value of the fund vs index over time** : obtained by calling `fund.export_to_csv(output_path)`
        
        <img src="/src/csv_output.png" alt="CSV Output of Fund Performance"/>
        
        Note that the variable `output_path` in `.export_to_csv` is set to 'data/historical-paper-values.csv' by default.
        
        **2a. View the DataFrame directly**
        
        The attribute `.all_assets_normalised` represents a DataFrame of the paper value of your fund vs the index.
        Use `print(fund.all_assets_normalised)` to view the DataFrame directly rather than exporting it as a CSV.
        
        
        ## Sample Code:
        
        ```
        #Imports
        import pyportfoliotracker
        from pyportfoliotracker import Fund
        
        #Set up your fund
        date = '2020-05-18'
        fund = Fund(2375706,'^FTSE',date,strategy='dca10')
        
        #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 any equities
        fund.sell_equity('GSK.L','2020-05-21',300,1670.20)
        
        #Call methods based on output desired
        fund.plot_fund_performance()
        fund.export_to_csv()
        
        #To view data in Python command line rather than in a csv:
        print(fund.all_assets_normalised)
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
