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Data-based investment method that can be used for a lifetime with Python quant investment

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

(5.0) 11 reviews

291 learners

  • Jiwoon Jeong
Quant
Investment
Pandas
Python

Reviews from Early Learners

What you will learn!

  • Investment techniques using Python & Pandas + financial data

  • Ability to develop your own investment hypothesis and run data-based simulations (Back-Test)

  • Various practical investment strategies and quantitative performance measurement methods (annual compound returns, drawdown)

  • Implement basic investment strategies (diversification, bond mix, rebalancing, trend following)

  • Implementing systrader79's average momentum score investment strategy

  • Implementing static asset allocation techniques (Permanent, Golden Butterfly, All whether)

  • Implementing dynamic asset allocation techniques (GTAA, FAA, VAA, DAA)

How do ordinary people invest? 👀

Many people already know that financial planning is essential. So, they've followed others and invested in stocks and coins. But why do stock prices always fall whenever I buy them? Is there no way for ordinary people to make money through investing?

Investing isn't about making a lot of money all at once. It's about long-term investments that protect against inflation and preserve your valuable assets .

[Example of long-term upward investment returns: All Weather Portfolio]

Most investment failures stem from being swayed by the news or the sentiments of acquaintances, leading to investment decisions without a clear set of criteria . However, with our busy lives, it's difficult for us to assess value and invest through corporate analysis. Therefore, I propose a quantitative investment approach, rather than a qualitative one: quant investing .

Quant investing,
I want to know that 💫

The greatest advantage of quantitative investing is that it provides objective, data-driven investment judgment criteria. Utilizing quantitative analysis allows you to make informed investments.

Everyone knows that to be successful in investing, you have to buy when it's cheap and sell when it's expensive .
Let me give you an example. The price of Stock A is lower than the price I checked a few days ago.
Then I judge this to be cheap (subjective judgment) and buy it bravely.
Of course, if you're lucky, you might make a profit, but the data tells us otherwise.

Experiment 1

Buy when it's cheap (falling) and sell when it's expensive (rising).

  • Experimental period: November 2002–July 2022
  • Subject: KOSPI 200
  • Buying Conditions: Today's Stock Price <= 20-Day Low (Buy when the stock price is low.)
  • Sell Condition: Today's Stock Price >= 20-Day High (Sell when the stock price is high.)


Cumulative return 1.56 (56%)

The strategy of buying when low and selling when high yielded a 56% return over approximately 20 years. Converting this to annual interest rates, it translates to approximately 2.3% compounded annually. This is similar to the deposit interest rate. However, considering our labor costs, which involved trading while monitoring the highest and lowest prices for 20 years, this doesn't seem like a particularly impressive return.

Experiment 2

Buy when it's expensive (rising) and sell when it's cheap (falling).

  • Experimental period: November 2002–July 2022
  • Subject: KOSPI 200
  • Buying Condition: Today's Stock Price >= 20-Day High (Buy when the stock price is high.)
  • Sell Condition: Today's stock price <= 20-day low (I will sell when the stock price is low.)


Cumulative return: 3.48 (348%)

It's amazing. Compared to the 56% return in Experiment 1, this Experiment 2 yielded a 348% return. Converting this to an annual interest rate, it translates to approximately 6.4% compounded annually. I believe this amount more than covered the labor costs associated with monitoring the highest and lowest prices over the past 20 years.

As you can see, utilizing quants allows you to make data-driven investments . It allows you to invest based on objective data and evidence , rather than subjective judgments. This empowers you to test your hypotheses before making actual investments.

To these people
I recommend it 😊

Long-term usable
Anyone who wants to develop an investment strategy

Invest with intuition every time
Anyone who has experienced loss

I have an investment strategy that I have been thinking about.
Those who gave up due to technical limitations


Data-driven with investment and coding
Anyone who wants to learn smart investment methods

What you'll learn 📚

This course covers fundamental investment theory and strategies. You'll learn how to implement and backtest various investment strategies developed by leading investment experts (e.g., All Weather, DAA, etc.) using Python and the Pandas library.


1. Concept and implementation method of investment performance indicators

  • Day-Return, Cumulative-Return, CAGR, DD, MDD
  • Before exploring various investment strategies, let's learn about indicators that measure quantitative performance.

2. Fundamentals of Investment - Diversification

  • You've probably heard a lot about diversifying your investments, but have you ever personally tested the difference between diversifying and not diversifying?
  • Samsung Electronics All-in-One vs. Diversified Investments in Five Major Companies

3. Investment Basics - Bond Mix

  • One of the reasons why stock investments are not sustainable for a long time is because of the excessive volatility.
  • What happens if you mix bonds?

4. Fundamentals of Investment - Rebalancing

  • They say that buying stocks and just holding them isn't the best option.
  • In theory, periodic rebalancing, which adjusts the ratio, can lead to better performance by selling high and buying low. Check out the results with actual data.

5. Investing Basics - Trend Following

  • As seen in the 20-day high and low price trading experiment, stock prices have trends, and investing accordingly yielded good results.
  • There are various trend following techniques, including absolute, relative, and dual momentum techniques.
  • Among them, we will implement the average momentum score strategy introduced by systrader79.

6. Practical Investment Strategy - Static Asset Allocation Strategy

  • Now that you've learned some basic investment strategies, it's time to implement a practical asset allocation strategy.
  • Implement and compare the performance of the Permanent, Golden Butterfly, and All Weather portfolios.

7. Practical Investment Strategy - Dynamic Asset Allocation Strategy

  • Likewise, it's time to implement a dynamic asset allocation strategy that can be used in practice.
  • Implement GTAA, FAA, VAA, and DAA strategies and compare performance.

8. Visualization of returns by period

  • The actual investment will take several years.
  • Therefore, it is also important to understand the recent performance of each strategy.
  • Learn how to visualize recent returns by month and year.


Changes in students after attending the lecture 📜

  • This explains why managing risk is more important than achieving high returns for sustainable investment.
  • Understand and explain the theoretical basis for diversification, asset class mixing, rebalancing, and trend following.
  • You can query and utilize financial data with Python.
  • Understand various dynamic and static asset allocation strategies, implement them in Python code, and backtest them.
  • You can create your own investment strategy by customizing the asset allocation strategies you learned in class.
  • You can now make informed investments based on data, rather than blindly investing.


Q&A 💬

Q. What Python development environment do you use?

I use Jupyter Notebook! It's convenient to install it through Anaconda.

Q. Can I take the course even if I don't have any basic knowledge of Python or Pandas?

The lecture assumes basic knowledge of Python programming syntax and Pandas.
If you need basic Python and Pandas content, please refer to the latter part of the curriculum!

Q. Isn't quant something difficult that only science and engineering students can do?

This course covers basic statistics at the middle/high school level, such as the average, variance, and normal distribution, and is suitable for anyone who has ever invested in stocks.

Q. Is this a course on creating an automated trading program?

No! This course covers asset allocation strategies based on data analysis. It differs from swing trading or scalping, which have very short trading cycles. The quantitative program we'll cover calculates your investment allocations at the end of each month, quarter, or year, depending on your investment strategy. You can then trade directly through your brokerage firm based on those allocations! We also plan to develop a short-term automated trading program course in the future. :)

Recommended for
these people

Who is this course right for?

  • People who have not properly learned about investment management but have experienced investment losses and want to learn smart investment methods

  • People who want to learn logical and systematic investment methods using coding and data.

  • People who are familiar with Excel and programming and want to turn that into financial skills

  • People who want to study their own investment strategy and make actual investments

  • People who want to make investments that will grow over a long period of time, rather than making and losing money all at once

Need to know before starting?

  • Python basic grammar (variables, loop, condition, function..)

  • Pandas basic syntax (series, dataframe and its associated concepts)

  • Knowledge of mathematics, probability/statistics at middle/high school level (not very difficult, but requires basic mathematical ability necessary for calculating returns and implementing portfolio logic)

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Curriculum

All

52 lectures ∙ (6hr 1min)

Course Materials:

Lecture resources
Published: 
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Reviews

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11 reviews

5.0

11 reviews

  • 김정빈님의 프로필 이미지
    김정빈

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    수고하셨습니다.

    • hakjuknu님의 프로필 이미지
      hakjuknu

      Reviews 155

      Average Rating 5.0

      5

      98% enrolled

      Great!

      • dukim님의 프로필 이미지
        dukim

        Reviews 3

        Average Rating 5.0

        5

        62% enrolled

        강의 지루하지 않고 재미있습니다. 자산 배분의 백테스팅 및 시각화에 대해서 설명을 하는데 너무 잘 설명해 주셔서 쉽게 이해 됩니다. 요즘 연금 저축과 개인irp 많이 하실텐데 직접 백테스팅 해보고 투자하셔도 좋을 것 같습니다. 국내ETF들이 최근에 좋은 상품들이 많이 나오기에 이에 상응하는 해외 ETF에 환율 적용해서 CAGR과 MDD를 계산하고 투자 비중을 결정했습니다.

        • nodlekang님의 프로필 이미지
          nodlekang

          Reviews 1

          Average Rating 5.0

          5

          100% enrolled

          쉽지 않은 내용을 쉽게 설명하기 위해서 많이 노력한 게 느껴집니다. 감사합니다.

          • jesstopu0414님의 프로필 이미지
            jesstopu0414

            Reviews 1

            Average Rating 5.0

            5

            10% enrolled

            깔끔하고 목소리도 좋으셔서 집중 잘 됩니다!

            $51.70

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