Building simul: A Real-Time Trading Simulator from Scratch

Intro

Hey, DEV community! I’m excited to share my side project, Simul8or – a real-time day trading simulator built to give users a risk-free environment to practice trading strategies. This project is 100% built on a tech stack of ASP.NET WebForms, C#, JavaScript, CSS, and SQL Server, with no external libraries or frameworks. It’s been a rewarding journey building it from scratch, and I wanted to dive into the technical side for those interested.

Why Build a Trading Simulator?

Day trading can be risky, especially for beginners. I wanted to create a platform where users could simulate trades using real-world data without putting their finances on the line. The goal was to mimic a realistic trading experience with performance analytics and interactive charts, all while keeping the development as lightweight and optimized as possible.

Tech Stack and Architecture

1. ASP.NET WebForms (Backend) Framework Choice: ASP.NET WebForms was chosen for its robust event-driven programming model, which aligns well with the trading actions (buy/sell requests, portfolio updates) and supports fast prototyping. API Endpoints: Custom endpoints handle data retrieval and user requests, ensuring that real-time data can be fetched and processed without latency issues. Session Management: Each user’s session data (virtual portfolio, open trades) is managed through server-side state management, enhancing data security and scalability.
2. C# for Core Logic Data Processing: The core trading logic (calculating profit/loss, updating portfolio values) is implemented in C#. The language’s strong typing and efficiency make it ideal for managing financial calculations in real-time. Error Handling: Given that financial simulations are error-sensitive, the backend is reinforced with error-catching logic to ensure users get accurate, consistent data.
3. SQL Server (Database) Data Storage: SQL Server handles user information, portfolio history, and market data. Indexing and optimized queries ensure that real-time data retrieval is fast, even with a growing number of users. Market Data Caching: To handle high-frequency read operations, I implemented a caching layer for market data, reducing database load and improving response times.
4. JavaScript (Frontend) Pure JavaScript: Since I wanted to avoid external libraries, the front end is pure JavaScript, making the experience fast and lightweight. Dynamic Charting: Custom charting logic was implemented to show real-time price movements, portfolio changes, and historical trends. This includes interval-based refreshes to ensure the charts remain in sync with real market data.
5. CSS for Styling Minimalistic Design: CSS was kept lean to ensure a fast load time. The styling emphasizes usability, with clear data presentation and an intuitive layout that resembles typical trading dashboards. Responsive Layout: Media queries make the interface usable on both desktops and mobile devices, making it accessible to a broader user base. Challenges Faced and Solutions Real-Time Data Handling: Working with real-time data can be intensive, especially without external libraries. I tackled this by implementing an efficient caching system and optimizing queries to reduce database load.

Trade Simulation Accuracy: The accuracy of trade simulations is crucial. I developed custom algorithms to handle trade execution and price movements based on real-world principles, ensuring users get an experience close to actual trading.

Performance Optimization: Running all this without libraries means that performance tuning was essential. I used asynchronous processing wherever possible and minimized server requests by caching frequently accessed data.

Lessons Learned

Balance Between Realism and Performance: Achieving realism in trading without overloading the system was a delicate balance. Certain optimizations, like limiting the refresh rate on market data, were necessary to ensure the simulator remains smooth and responsive.

Importance of Error Handling: When working with financial simulations, accuracy is critical. Extensive error handling and validation mechanisms helped prevent any inconsistencies in user portfolios or market data representation.

What’s Next?

I’m planning to add more advanced features, like user-defined trading algorithms and additional chart types, while keeping the platform simple and intuitive. I’m also exploring ways to further optimize server resources to handle higher traffic volumes.

Feedback Welcome!

If you have any thoughts, feedback, or suggestions, I’d love to hear them. This project has been a fantastic learning experience, and I hope to make it even better with insights from the DEV community!

Try it out here: simul8or.com

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