Hey everyone,
A few friends and I are huge CS2 players and data nerds. If you’ve ever traded on the Steam Community Market, you know it’s prone to coordinated pumps and dumps. We wanted to see if we could build a tool to catch these market anomalies (accumulation phases) before the massive price spikes actually happen.
We built CSPump to scan the Steam Market and CSFloat for unnatural volume and listing behavior. I wanted to share a bit about our stack, the architectural choices we made to handle the background processing, and see if any other devs here play CS.
The Tech Stack
- UI / Styling: Next.js, TailwindCSS, shadcn/ui
- Authentication: better-auth (with a custom Steam plugin)
- Database & Queues: PostgreSQL with pg-boss
- Emails: AWS SES
- Payments: Stripe
- Observability: Pino.js + Loki transport + Grafana
The Technical Challenges
1. Steam Authentication with better-auth Steam still relies on an ancient OpenID 2.0 implementation, which makes modern authentication a bit of a headache. We decided to use better-auth, but since there wasn't an out-of-the-box solution for Steam, we had to write a custom Steam plugin for it. Getting the redirects, session state, and initial inventory sync to fire correctly without leaving the user hanging on a loading screen was an interesting challenge.
2. Background Workers & Queueing (Without Redis) Our app doesn't rely on users staring at heavy real-time dashboards. Instead, users configure watchlists, and our backend crunches the data. When an anomaly triggers, it dispatches an email alert. Instead of spinning up Redis for job queues, we opted for pg-boss, which uses PostgreSQL for background jobs. It allowed us to keep our infrastructure simple while effectively managing the massive amount of scheduled market scans and queuing up AWS SES email dispatches.
3. Observability & Logging Because the core value of the app relies on background workers executing flawlessly, we needed solid visibility into our queues. We set up Pino.js with a Loki transport, feeding logs directly into Grafana dashboards. This lets us monitor worker health, track Steam API rate limits, and see when our anomaly engine triggers in real-time without constantly querying the database directly.
Did it actually work?
Surprisingly, yes. The algorithms have caught some pretty crazy spikes early, and the SES email dispatches have been landing right on time. For example, our system caught:
- FAMAS | Survivor Z (FN): Flagged the accumulation phase right before it pumped 82%.
- Sticker | BIG (Holo) | 2020 RMR: Caught it before a 47% spike in two days.
- USP-S | Pathfinder (FN): Flagged before a 46% increase.
What’s next?
Right now, we are looking into how to handle marketing emails. We use React Email for templates, but we don't know the best approach for going about promotions, new features etc. Is it best to create a new template for this and then send it out manually or should we set up a dedicated marketing system? Also curious if anyone else here has used pg-boss instead of Redis for job queues and how it scaled for you!
If you play CS and want to check out how we implemented the UI, the project is live at CSPump.