u/cbrincoveanu

I've been diving into quantitative finance recently. If you look at algorithmic trading online, it's full of tutorials promising guaranteed returns, usually leaving you with unstructured spaghetti code that falls apart in live markets due to slippage and fees.

I wanted to move away from guessing and build a professional "laboratory" to rigorously test strategies, and then actually deploy them. I open-sourced the resulting infrastructure as a template.

GitHub Repo: https://github.com/cbrincoveanu/algo-trading-template

Full Write-up / Research: Algorithmic Trading with VectorBT and Lumibot

My initial goal was to see if I could find "Alpha" (beat the market) using standard technical analysis on the Magnificent Seven tech stocks. I set up the environment to run hyperparameter optimizations on moving average crossovers. The lab proved exactly what the Bogleheads always say: finding true Alpha is incredibly difficult. Once you account for trade friction (fees/slippage) and avoid the trap of overfitting historical data, a simple Buy & Hold or quarterly rebalancing strategy is incredibly hard to beat.

Even though my complex strategies didn't beat the market, the infrastructure to test them is solid, and I wanted to share it so others can use it for their own research.

The repo provides a fully reproducible environment (VSCode Dev Containers / Docker) broken into distinct phases:

Phase 1: The Research Lab. Uses VectorBT inside Jupyter Notebooks. It uses NumPy broadcasting to run thousands of backtest combinations (testing parameters, Sharpe ratios, etc.) in seconds.
Phase 2 & 3: Event-Driven Execution. Uses Lumibot to take the validated strategy and run an event-driven backtest (simulating realistic market ticks and slippage), and then deploy it live.
Broker: Pre-configured for Alpaca (Paper & Live trading via .env credentials).

I've included a "Magnificent Seven" simple rebalancing strategy out-of-the-box so you can see how the pipeline works:

Test your hypothesis in the notebooks/ folder.
Implement it as a production-ready class in the strategies/ folder.
Run python run_backtest.py to get an industry-standard tearsheet of your strategy's performance.
Run python run_live.py to deploy the bot.

I’d love to hear your feedback, code reviews, or thoughts on what else would make a quantitative template like this useful!

I noticed that many people seem to miss the classic Web 2.0 exploration era. It feels like the old internet is dying, and every search result right now is just a hyper-optimized affiliate link farm or AI-generated slop. You can't just stumble onto weird stuff anymore without hitting massive paywalls or SEO garbage.

So, I built Gulugulu.

It's basically a cheap clone of Google circa 1998. But the catch is that it only indexes stuff you'd actually want to stumble across. No clickbait, no lackluster blogs. Just digital gardens, personal sites, unhinged ascii art, and high-signal tools.

You can try it here: https://cbrincoveanu.github.io/gulugulu/

Tech-wise, there's no backend. It's a serverless jamstack MVP using Fuse.js for client-side search, meaning there are no databases, no ads, and absolutely zero tracking. To actually get the data, I wrote a python crawler that scrapes curated networks (like Cloudhiker and 512kb.club) and dumps the metadata straight into a flat JSON index.

Right now, the index is small enough that it doesn't melt your browser memory. I'm currently building out a deeper crawler with an LLM-scoring step to try to automatically detect and nuke SEO spam before it hits the index.

How you can help:
I desperately need to expand the seed list. I want this to be a useful search tool for the old, weird, captivating web.

Drop your favorite weird websites, your personal blog, or cool RSS feeds in the comments. Let me know what you think of the client-side search speed, too. I'll manually add the best links to the crawler.

(Also: hit the I'm feeling lucky button if you just want to instantly drop into a digital rabbit hole.)

I got tired of messy trading scripts, so I built a fully Dockerized algo-trading lab to test strategies mathematically before trading live.