Portfolio-Tearsheet-Return-Generator

Overview

This program creates a brief, two-page document for an investor to summarize the historical outcome of the investor's portfolio transactions over the selected time period.

Goals of this Project

1. Allow investors to input their tranaction data via a .csv file
2. Handle time-series transactions based on First-In-First-Out (FIFO) Logic
3. Utilize the QuantStats library to display portfolio statistics against a benchmark
4. Print the statistics output to a .pdf for simpler sharing

Function Explanations

def create_market_cal(start, end)

• Uses the pandas_market_calendars library to find all relevant trading days within a specified timeframe
• Automatically filters out non-trading days based on the market
• Sets NYSE as the calendar, and then standardizes the timestamps to make them easy to join on later

def read_csv(folder, csv_name)

• Takes a folder name and filename for where the .csv if located and creates a dataframe out of it

def get_data(stocks, start, end)

• Takes an array of stock tickers with a start and end date and grabs the data using the YFinance library

def position_adjust(daily_positions, sale)

• Create an empty dataframe called stocks_with_sales to which we'll add adjusted positions, and another dataframe holding all of the transactions labeled as buys
• Here we calculate the realized gain of each sell order

def portfolio_start_balance(portfolio, start_date)

• Add to the adjusted positions dataframe, the positions that never had sales, sales that occur in the future, and any zeroed out rows to create a record of your active holdings as of the start date

def fifo(daily_positions, sales, date)

• Flters sales to find any that have occurred on the current date and creates a dataframe of positions not affected by sales
• Then use position_adjust to zero-out any positions with active sales and append the positions with no changes, leaving you with an accurate daily snapshot of your porfolio positions
• Any rows with zero 'Qty' are left for realized gain performance calculations

def time_fill(portfolio, market_cal)

• Provide our dataframe of active positions, find the sales, and zero-out sales against buy positions.
• Loop through using our market_cal list with valid trading days
• Filter to positions that have occurred before or at the current date and make sure there are only buys.
• Add a Date Snapshot column with the current date in the market_cal loop, then append it to our per_day_balance list
• Before adjusting a position, we make sure to save all future transactions to another dataframe and append them later

def modified_cost_per_share(portfolio, adj_close, start_date)

• Merges provided dataframes and calculates daily adjusted cost

def portfolio_end_of_year_stats(portfolio, adj_close_end)

• Finds end date closing prices for each ticker

def portfolio_start_of_year_stats(portfolio, adj_close_start)

• Finds start date closing prices for each ticker

def calc_returns(portfolio)

• Applies a bunch of calculations against the data we’ve been modifying, and returns a final dataframe

def per_day_portfolio_calcs(per_day_holdings, daily_adj_close, stocks_start)

• Runs modified_cost_per_share, portfolio_end_of_year_stats, portfolio_start_of_year_stats, calc_returns and returns a daily snapshot of the portfolio

def format_returns(pdpc, metric_to_group1, metric_to_group2, div, total_cash)

• Formats the per day portfolio calculations for use with the QuantStats library by adding up the daily realized, unrealized gains, dividends and cash

def generate_report(returns, folder, rf=0.)

• Utilizes the QuantStats library to create a .html and .pdf of the tearsheet returns as well as display the output to the consol with quantitative statistics

How to use this project in Google Colab

1. Connect to a Runtime
2. Press Ctrl + F9

Example Quick Start Main

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Gather Data and Generate Report
generate_report('Tearsheet Generator', 'TransactionHistory2020-2022withCash', '2020-04-30')

Example Input .csv

Symbol	Qty	Type	Open date	Adj cost per share	Adj cost
AAPL	10	Buy	4/29/2020	71	710
MSFT	10	Buy	5/1/2020	175	1750
AAPL	5	Sell.FIFO	5/15/2020	77	385
...	...	...	...	...	...

Example Output

HTML

PDF

Tearsheet_2023-01-06.pdf

Libraries Used

• YFinance
• QuantStats
• PdfKit
• Pandas
• Pandas Market Calendars
• MatPlotLib

References

• https://towardsdatascience.com/modeling-your-stock-portfolio-performance-with-python-fbba4ef2ef11
• https://github.com/mattygyo/stock_portfolio_analysis/blob/master/portfolio_analysis.py
• https://github.com/ranaroussi/quantstats