Welcome back to Sean’s Syntax!
A lot has happened with my senior software project, and today I’m just going to give a quick overview of the progress we have made. Im also going to talk about why i chose this project and if it has met my expectations.
Introduction to Sean’s Syntax
This was my first post, in which I introduced myself and what was in my mind for this blog. I wrote about having lived in San Diego, surfing and photographing as a hobby, and being psyched about working with AI or algorithm business. I also presented the three pitches that interested me most, like developing a cloud-based algorithmic trading application.
Algorithms in Stock Trading
Next, I explained what the basis of what our project involves—optimization and algorithms. I explained that we’re testing different trading strategies with a genetic algorithm and picking out the best. We’re tracking important statistics like Sharpe ratio, max drawdown, and win rate to see how each strategy does. This blog established how we’re going to be making wiser trading decisions.
Creating Clouds (AWS Setup)
After that, I talked about configuring AWS Elastic Beanstalk for our trading application. We had not deployed our application, and discussed how we had established our environment, i.e., choosing our platform to be Python, defining environment variables, and real-time monitoring. This will be applicable in deploying and scaling our algorithm.
Coding Culture and Clean Code
In this blog, I took a different direction and wrote about best practices in clean code. Following in the footsteps of Robert C. Martin’s Clean Code and Martin Fowler’s Refactoring, I wrote about naming and why descriptive naming makes our code readable and maintainable. I warned against “shotgun surgery”, a code stench in which you have to do a single thing in many locations. By focusing on cleaner code, we’re keeping our project organized and saving ourselves enormous headaches in the future.
The Tech Stack
Most recently, I walked through our tech stack. My favorite tool is Python because it’s easy to read and gets along with libraries like NumPy, Pandas, and backtesting.py. SQL’s fantastic to work with when you have massive amounts of data, but it’s not a favorite because it’s a bit clunky. Lastly, AWS was a painful learn, but now it’s a lifesaver for deployment and scaling. These tools combined power our trading algorithm from start to finish.
Why This Project & Expectations So Far
I chose this project because it perfectly blends my interests in algorithms, finance, and software engineering. I’ve always wanted hands-on experience with building smart systems, and algorithmic trading felt like the perfect challenge. Since there’s real data, real complexity, and real-world impact. So far, it’s absolutely met my expectations. I’m learning a ton about coding best practices, cloud infrastructure, and how to design algorithms. I’m excited to see how our trading strategies perform in real-world tests. Thanks for following along, and stay tuned for more updates on Sean’s Syntax as we continue building this project!
