代写FIN 400 – ALGORITHMIC TRADING WITH PYTHON帮做Python编程

UNDERGRADUATE COURSE SYLLABUS

FIN 400 – ALGORITHMIC TRADING WITH PYTHON

COURSE DESCRIPTION

Algorithmic trading is the use of predefined software instructions to execute trades faster than humans, eliminating manual market scanning and reducing emotional biases. Topics covered include strategy design, backtesting, and real-world implementation.

AUDIENCE

•    Students interested in pursuing careers in finance, particularly in roles that involve quantitative analysis and trading strategies,

•    Professional traders, analysts, and portfolio managers looking to automate their trading strategies,

•    Business Analytics majors who would like to apply their skills to financial markets and automated trading,

•    Finance, economics, and computer science majors who want to explore the intersection of technology and trading.

•    Individual Investors looking to develop their own automated trading systems to manage and optimize their portfolios.

•    Entrepreneurs and Startups interested in building financial technology (FinTech) solutions that involve automated trading capabilities.

•    Financial Enthusiasts who want to gain practical knowledge of algorithmic trading.

A prior knowledge of the Python programming language or a basic understanding of financial markets is not required.

CREDITS 3

LEARNING OBJECTIVES

Upon successful completion of the course, students will be able to:

• Collect and analyze historical financial data using Python.

•     Apply portfolio optimization techniques in Python to determine optimum investments.

•     Understand the potential benefits of algorithmic trading for managing investments.

•     Explore different types of trading strategies (e.g., Bollinger Bands, Relative Strength Index).

• Conduct backtesting to evaluate the historical performance of trading strategies.

•     Identify and address issues such as slippage, transaction costs, and latency in trading strategies.

•     Optimize trading strategies by adjusting parameters to enhance performance metrics like profitability.

• Transition from backtesting to live or paper trading.

• Apply automated trading by writing Python scripts on the Alpaca Trading API.

COURSE REQUIREMENTS

A free, paper-trading account is required on Alpaca: https://alpaca.markets/

We will use the Python programming language.  Prior experience in a programming language is not required.

A free, cloud version of Python is available at: colab.research.google.com/ We will use the following Python libraries / packages :

alpaca-py   empyrial    numba       numpy       pandas

plotly      pyfolio     skfolio     vectorbt    yfinance*

* : Must be version 0.2.40 or earlier.

GRADING

1- HOMEWORK (5%)  : Most homework assignments allow unlimited attempts, and the answer key is visible when you submit an attempt.

2- KAHOOT (15%)  : There will be in-class Kahoots.  The Kahoots will assess the content taught in class.  All students are responsible for following the instructions and completing the assignments.  Late-arriving students are responsible for completing the missed work. If your code returns an error message, you are responsible for fixing it before that lecture's Kahoot. You can consult your classmates or review the lecture materials posted on Blackboard.

This is to ensure that all students arrive on time and follow the lecture without getting distracted.

If you are unable to fix errors in your code, the Professor will help fix them after that lecture's Kahoot.

In some classes, there may be multiple Kahoots, one at the beginning of the class and another one at the end of the class. Missed Kahoots due to unexcused absences will count as 0.  Students will be exempt from a Kahoot when:

They travel for university-related work (e.g. student athletes),

They have religious observance,

They have a physician's note explicitly advising them not to attend class (a note  confirming a visit to a healthcare facility is not sufficient).

They have documented emergency.

In addition/ three of the lowest Kahoot scores will be dropped from every student. For instance, John Doe has the following Kahoot scores:

In this case, three of the lowest Kahoot scores and the missed Kahoots due to excused absences are dropped (highlighted above).

John Doe's Kahoot score equals  3 + 2 + 1 + 4 + 3 + 3 + 4  = 20/28 = 71.4% Your Kahoot username should be your netID.  For instance, if your SU email is [email protected] ,  then your username should be johnd. If NetID is incorrectly input, the score is registered as 0 and may not be changed later.

Remote participation in Kahoots is violation of academic integrity, and is not allowed. Students who are more than 20 minutes late to class will not receive credit for that day's Kahoot even if they participate in it.

All students should use laptops instead of cell phones when playing Kahoot. Otherwise, cell phones may occasionally get disconnected from Kahoots.

Grade adjustment is not possible on the Kahoots.

For instance, the following will not result in grade change:

"I typed the correct answer, but it did not register. "

"I ran out of time". "I entered my username wrong. "

"I used a cell phone and got disconnected from the game. "

"I was in the bathroom / outside of the classroom when the game started. " "I typed the correct answer as a decimal ratio, but the question instructed the answer to be input as percentage. "

3- MIDTERM #1 (10%)  : The midterm is an open-notes, open book test.  A PC with sufficient computational power, Google Colab, and strong internet connection are required during the test.  The test allows a single attempt;  a second attempt cannot be given. No exceptions.

4- MIDTERM #2 (15%)  : The midterm is an open-notes, open book test.  A PC with sufficient computational power, Google Colab, and strong internet connection are required during the test.  The test allows a single attempt;  a second attempt cannot be given. No exceptions.

5- CUMULATIVE FINAL (55%)  : The final exam is an open-notes, open book, cumulative test.  A PC with sufficient computational power, Google Colab, and strong internet connection are required during the test.  The test allows a single attempt;  a second attempt cannot be given. No exceptions.

Your course grade (out of 100) will be computed as follows:

COURSE GRADE =  HOMEWORK * 0.05  +   MIDTERM #1 * 0.10   +   KAHOOT * 0.15 +   MIDTERM #2 * 0.15  +   CUMULATIVE FINAL * 0.55

The letter grades are assigned based on ranking; and not based on the absolute value of the grades.  Therefore, there is no need to adjust test scores by, for instance, adding 10 points to everyone’s score since that would not change the ranking of the students.

The faculty at the Whitman School developed a uniform. grading policy for the undergraduate program. The policy has three goals: (1) to ensure that grading is fair and consistent across courses, (2) to encourage students to take their coursework seriously, and (3) to ensure faculty deliver a challenging academic experience.

For all undergraduate courses taken at the Whitman School with 25 or more students enrolled:

• The average class grade shall be no higher than 3.30

• The maximum percentage of A/A- is 33.00%

After the final exam, all students will be ranked based on their semester grades.

•    For undergraduate courses with class sizes of 25 or more, Whitman School of Management does not have predetermined "cutoff" limits for letter grades.

• Your letter grade will be based on your combined ranking in the class.

Your letter grade will not be based on the absolute value of your final grade.

•    For instance, a final grade of 96/ 100 would NOT result in an A if that does NOT place you within the top 33% among the combined sections taught by the same professor.

•    Likewise, a final grade of 78/100 may result in an A if that places you within the top 33% among the combined sections taught by the same professor.