Environment Setup

1. install anaconda
2. create & activate a virtual environment
3. install quantstats lib: pip install quantstats --upgrade --no-cache-dir
4. open this repo with your favourite python IDE, I use pycharm
5. in your python IDE, set up the project interpreter as your created virtual environment, which is where you installed quantstats

How to run

1. open main.py, go to the main function
2. set three global variables: risk_free_return, year, quarter
   • risk_free_return: 3-month treasury bill yield. It's updated daily. To get the latest yield, click here
   • year: how many years back do you want to get the data. max is 10
   • quarter: a boolean value to toggle quarter or annual report for company fundamentals
3. adjust the number variable below the global variables to run what you need.
4. You will need to read on to know what each number indicates.

Company Fundamentals Analysis

• analyze a company's fundamentals
  • set number = 1.1, fill in the function with a company stock ticker
  • run main.py
  • it will print historical financials including balance sheet, income statements, & cash flow statements
  • the console also shows the following analysis:
    • profitability:
      • Gross Margin
      • Cost Margin
      • Net Profit Margin
      • Expenses Margin
      • ROE
      • ROA
    • operating:
      • Receivables Turnover Days
      • Inventories Turnover Days
      • Total Assets Turnover Days
    • solvency:
      • Liability/Asset Ratio
      • Current Ratio
      • Acid-test Ratio
    • growth:
      • Revenue Growth
      • Net Income Growth
      • Operating Income Growth
      • Free Cash Flow Growth
    • investing:
      • wacc
      • roic
      • excess return
      • economic profit
      • stockholders equity growth
      • dividendYield
      • dividendPayoutRatio
      • Number of Shares
      • Market Capitalization
      • Enterprise Value

Companies' Fundamentals Comparison

• set number = 1.2
• either prepare a .txt file containing company tickers at each line, or specify the company tickers in ticker_list variable
• run main.py
• it will produce a table comparing different companies profitability, operating capabilities, solvency and investment value
• the comparison provides both mean and latest values
• below picture shows an example

Plot Assets Correlations

• set number = 3.1
• fill in tickers of interested in asset_tickers variable
• run main.py
• it will generate an image describing the pair-wise assets correlations
• below picture shows an example

Optimize Portfolio with Modern Portfolio Theory

• My slides on Modern Portfolio Theory: click me

  • this slide introduces in great details and a friendly way to help understand & apply MPT optimization

• optimizing risk

  • when optimizing risk, the optimizer will find the optimal weights for the assets to minimize the portfolio risk
  • set number = 3.2
  • set asset_tickers to be a list of tickers of interest
  • run main.py
  • in the console, optimal weights for each assets will be provided
  • the efficient frontier will also be plotted (will only plot efficient frontier for less than 4 assets)
  • below pictures show an example

  

• optimizing sharpe ratio

  • when optimizing sharpe ratio, the optimizer will find the optimal weights for assets to maximize the portfolio sharpe ratio
  • set number = 3.3
  • set asset_tickers to be a list of tickers of interest
  • run main.py
  • in the console, optimal weights for each assets will be provided
  • It will also provide the risk return of portfolios in different ratios of risk-free asset and risky assets
  • the efficient frontier, and the capital market line will be plotted
  • below pictures show an example

Evaluation for Modern Portfolio Theory

• why do evaluation?
  • modern portfolio theory did change how people invest, by focusing on diversification, risk and return, etc
  • however, modern portfolio theory does has its assumptions (which is too ideal), and limitations due to its assumptions
  • since we used MPT to optimize our portfolio, we need to understand how well MPT works on the selected assets
  • that's the need to do the evaluation
  • I suggest you to do evaluation for every assets combo you would like to invest
• evaluation methods explained
  • or a year yi (i >= 2) in a series of years: [y1, y2, y3, …, yn]
  • get prediction:
    • use history data [y1, y2, …, yi-1] to find the optimal weights
    • apply these optimal weights to the data in year yi, so as to get predicted risk, return, & sharpe
  • get ground truth:
    • use only data in year yi to find the optimal weights in that year
    • apply these weights to the data in year yi, so as to get the best-possible portfolio in that year
  • we compare prediction and ground truth
• how to run evaluation
  • set number = 3.5
  • fill in asset_ticers with ticker strings of interest
  • run main.py
  • this will generate 4 pictures
    • risk optimization
      • a figure shows to provide evidence on how well the MPT tracks risk-optimized ground truth.
      • another figure plots the return of the portfolio if using MPT risk-optimized weights, and ground truth weights
    • sharpe ration optimization
      • a figure shows to provide evidence on how well the MPT tracks sharpe ratio optimized ground truth.
      • another figure plots the return of the portfolio if using MPT sharpe ratio optimized weights, and ground truth weights
• what to observe from the 4 pictures:
  • if optimize portfolio by minimizing the risk, the predicted portfolio risk should be higher than ground truth
  • if optimize portfolio by maximizing sharpe ratio, predicted portfolio sharpe should be lower than ground truth
  • the closer the prediction tracks the ground truth, the more evidently that MPT works in terms of predicting the best portfolio
• below pictures show an example

Enjoy! :-)

RY