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.
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)
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 .
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 .
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).
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).
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.
Long-term usable
Anyone who wants to develop an investment strategy
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
2. Fundamentals of Investment - Diversification
3. Investment Basics - Bond Mix
4. Fundamentals of Investment - Rebalancing
5. Investing Basics - Trend Following
6. Practical Investment Strategy - Static Asset Allocation Strategy
7. Practical Investment Strategy - Dynamic Asset Allocation Strategy
8. Visualization of returns by period
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. :)
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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Bài giảng vui nhộn, không nhàm chán. Việc kiểm tra ngược và trực quan hóa việc phân bổ tài sản được giải thích rõ ràng đến mức dễ hiểu. Ngày nay, bạn có thể tiết kiệm được rất nhiều tiền lương hưu và IRP cá nhân, vì vậy sẽ là một ý tưởng hay nếu bạn tự kiểm tra lại trước khi đầu tư. Do nhiều sản phẩm tốt đã được phát hành gần đây trong các quỹ ETF trong nước nên chúng tôi đã tính toán CAGR và MDD bằng cách áp dụng tỷ giá hối đoái cho các quỹ ETF nước ngoài tương ứng và xác định tỷ lệ đầu tư.
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