r/coolgithubprojects

▲ 0 r/coolgithubprojects+1 crossposts

LightBase: Lightweight API & Database Desktop Client

🚀 LightBase

Ultra-performance, bare-metal local development runtime bridge and persistence engine built for high-compute applications.

LightBase decouples heavy disk and outbound network I/O into a standalone, multi-threaded C background daemon, communicating asynchronously with an API gateway over high-speed Linux Unix Domain Sockets (UDS).

🏗️ Architecture Layout

LightBase eliminates high-level framework overhead, garbage collection cycles, and process-blocking runtime constraints by decoupling tasks into independent layer boundaries.

graph TD
    UI[HTML5 Control Center UI] <--> PY[Python Gateway Bridge Server]
    PY -- "Binary TLV Frame Stream (/tmp/lightbase.sock)" --> CORE[C-CORE ASYNC THREAD POOL]
    
    subgraph "C-CORE (libcore.so)"
    CORE --> R1[Arena-Powered SQL Engine]
    CORE --> R2[Stack-Isolated OpenSSL TLS]
    CORE --> R3[Kernel /proc Ring Log Storage]
    CORE --> R4[Database Catalog Scanner]
    end

Layer Responsibilities

  • Frontend UI Layer: A lightweight development client executing async network operations back and forth.
  • API Gateway Layer: A zero-dependency Python routing engine acting as an IPC proxy gateway.
  • Native Systems Core: A high-speed, bare-metal C shared engine processing dynamic allocations, filesystem transactions, and socket forging loops on detached POSIX background threads.

🚀 Core Studio Module Suite

🌐 Module 1: Bare-Metal Environment Manager

Tracks application environments (Development, Staging, and Production) dynamically inside isolated runtime boundaries.

  • Atomic Remapping Swaps: Eliminates sluggish filesystem configuration lookups by pre-allocating an EnvironmentBlock structure directly within a dedicated MemoryArena. Context switches occur in under 1μs via thread-safe atomic pointer reassignments.
  • Fallback Safety Paths: System state transitions are fully guarded against uninitialized pointer exceptions, ensuring backup file parameters handle initial transaction states safely.

🧪 Module 2: Cryptographic API Testing Studio

Provides bare-metal HTTP engine request assembly and transaction benchmarking.

  • Wire Packet Serialization: Leverages stack-allocated sequence spaces to assemble raw wire payloads (GET, POST, PUT, DELETE) with exact specification alignment.
  • Memory Security: Replaces vulnerable variable concatenation with explicit length-bounded tracking (strncat), preventing string overflows when importing large custom data tokens.

🗄️ Module 3: Log-Structured Database Explorer

Powers interactive sidebar schema catalog visualizers and data grids.

  • Catalog Data Harvesting: Bypasses table-scanning bottlenecks by executing targeted schema scans directly against the internal engine catalog (sqlite_master).
  • Tele-Profiling: Measures precise VM bytecode query times using high-resolution monotonic hardware timers (clock_gettime).

🗄️ Append-Only Ring Buffer Telemetry Storage

Ensures high-throughput execution tracking without destroying flash storage sectors.

  • Static File Sizing: Pre-allocates a fixed array block file footprint on disk exactly once during system boot, guaranteeing predictable allocation.
  • Wrap-Around Bit Algebra: Sequences incoming snapshots using sliding timestamp indexes. When the tracking pointer reaches limits (1024 slots), it wraps back to slot 0 instantly.

📊 Performance Benchmarks

LightBase delivers sub-millisecond core processing speeds by bypassing the local network routing stack:

  • Local Database Transaction: ~990.20 μs ($< 1\text{ ms}$ bare-metal execution)
  • Total IPC Roundtrip Gateway Latency: ~1.203 ms (Inclusive of Python decoding and HTTP transport)
  • Outbound HTTP Network Socket Request: Variable based on distance, wrapped with microsecond-accurate tracking via CLOCK_MONOTONIC.

🛠️ Compilation & Installation

Prerequisites

Ensure your host machine runs a modern Linux kernel with cmake, gcc, and uv:

sudo apt update &amp;&amp; sudo apt install cmake build-essential

1. Build the Production Core Library

LightBase employs an out-of-source CMake build pipeline with Link-Time Optimizations (-O3 -march=native -flto -s):

cd core
mkdir -p build_release &amp;&amp; cd build_release

# Configure and build the target layout
cmake -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --target install

Assets generated:

  • Public Header: dist/include/engine.h
  • Shared Binary: dist/lib/libcore.so

2. Boot the Intermediary Python Gateway

cd ../../bridge
/usr/bin/uv run python python_bridge.py

The C-Core instantly carves out a high-speed memory socket at /tmp/lightbase.sock upon initialization.

🧠 Memory Design & Safety Assertions

  • Header Symbol Ordering: Enforces a strict structure order: macro directives first, raw packed structural records second, and export function interfaces last to ensure top-down layout translation.
  • Translation Scope Reductions: Centralizes cross-module variables within a shared master header file to keep dependencies clear.
  • Thread Race Protection: Active server file descriptors pass explicitly into separate heap memory pools (malloc) at worker thread creation, isolating context pointers from stack invalidation faults.

📄 License

Licensed under Apache License 2.0

github.com
u/Ok_Sky3062 — 1 hour ago
▲ 941 r/coolgithubprojects+3 crossposts

Open Source Palantir on Git

Open Source Palantir

We're building OSIRIS - The Open-Source Palantir Alternative

Feel free to Pull Request the team will review and merge if applicable 🙏

Just launched at osirisai.live - a free, open-source global intelligence platform:

-Real-Time Tracking:

-10,000+ commercial, military and private aircraft live on a 3D globe

- 2,000+ satellites including ISS

- 1,400+ worldwide CCTV camera feeds

- Earthquakes, wildfires, nuclear facilities and severe weather

Built-In OSINT Tools (no installs needed):

Nmap port scanning from the browser

- DNS record lookup and enumeration

- WHOIS domain intelligence

- SSL/TLS certificate transparency

- BGP routing and ASN lookup

- Threat intelligence and IP reputation

All running on a 3D interactive globe with day/night cycle, 20+ live API feeds, and a SIGINT news aggregator.

Live: https://osirisai.live

GitHub: https://github.com/simplifaisoul/osiris

Free. Open Source. No sign-up required.

u/Gold-Comfortable-340 — 10 hours ago
▲ 127 r/coolgithubprojects+2 crossposts

If you use mac, you can get subway data on your terminal

Lived in NYC for years before moving away. Never got over the specific brand of platform rage, close laptop, walk over, tap in, doors close in your face, next one's in 14 minutes, Always 14.

So I built a tiny tool for Mac users who already live in the terminal. You type in the station, and it shows the next trains in both directions, for every line, color-coded by how panicked you should be. Can also plan an end-to-end route with transfers like the MTA Trip Planner, but on the terminal.

How to Install:

brew tap itsashishupadhyay/mta
brew install mta

Some Commands to try:

mta -s "Times Sq"         # next trains at every line, both directions
mta -s "bedfrod avr"      # fuzzy match, misspell, similar stations
mta -s "bay prkwy"        # 3 stations called Bay Pkwy. picker tells you which is which
mta -s "Penn Station" -d "DeKalb Av"           # end-to-end route plan with         transfers
mta -s "Times Sq" -d "Bedford Av" -v           # same but verbose. per-stop diagram, the prettiest output
mta -s "Bay Ridge-95" -d "Forest Hills" -t 10  # only show trains leaving 10+ min from now
mta -s "14 St-Union Sq" -l L                   # filter to one line. less scrolling
mta --help                                     # everything else

Repo + screenshots: https://github.com/itsashishupadhyay/NYC_MTA_Timetable

Please try it on your home/work/regular station, and please reply with what's wrong, missing, or weird.

u/Delicious_Syrup_6766 — 9 hours ago
▲ 168 r/coolgithubprojects+1 crossposts

Visit https://knock-knock.net for live data.

A few months ago I posted a web site that I built to show hostile bot traffic hitting my seemingly unprotected VPS. Since then, I have expanded the honeypot to support Telnet, FTP, RDP, SMB, SIP, HTTP, SMTP, in addition to SSH. The site now shows all of my idlers (8) feeding into a single aggregate display. See the bots' locations, favorite usernames, passwords, an ISP Wall of Shame, and more, all of which can be filtered by protocol.

Click the speaker icon to hear a virtual geiger counter detect what has been called "the background radiation of the internet". If the data is coming in too fast, hit the pause button or space bar.

The source is available here: https://github.com/djkurlander/knock-knock

Have fun, and send comments or questions.

u/Desperate-Second-887 — 13 hours ago
▲ 230 r/coolgithubprojects+7 crossposts

Built a React data grid that can save you hours of time and money.

Hello everyone,

Wanted to share a super cool project (IMO) we have been working on. It’s a zero-dependency React data grid, called LyteNyte Grid. Check it out, and hopefully, you will find it useful and save yourself a ton of time.

Some of the reasons to use LyteNyte Grid.

  • Crazy Performance: LyteNyte Grid is super light at only 40kb (gzipped) and is extremely fast. It can handle millions of rows and 10,000+ updates/sec. Based on our internal benchmarks, it is one of the fastest grids available on the market.

  • Feature-rich: Brings 150+ features, most of which are free and open source. Features such as cell range selection, row master-detail, and row grouping are included for free with LyteNyte Grid. This is something we are quite proud of. There are paid libraries (I won't name them) that offer less.

  • No Styling Tradeoffs: With LyteNyte Grid, you can choose whether to go headless or styled. There is basically no tradeoff when considering styling choices.

  • Full Prop Driven: You can configure it declaratively from your state, whether it’s URL params, server state, Redux, or whatever else you can imagine, meaning zero sync headaches.

  • Unique DX Experience: Our grid is built in React for React and has a clean declarative API, which eliminates awkward configuration workarounds.

We also recently dropped LyteNyte Grid AI Skills. This is a really nice feature if you’re using AI coding agents. It lets you describe an advanced data grid solution, and your AI agent codes it for you. We have been testing this with increasingly complex grid instances, and the results have been awesome.

All our code is publicly available on GitHub. Happy to answer any questions you may have.

If you find this helpful and like what we’re building, GitHub stars help. Feature suggestions and code contributions are always welcome.

1771technologies.com
u/Vis_et_Honor — 16 hours ago
▲ 40 r/coolgithubprojects+2 crossposts

I built a small OSS tool to simplify Windows OS deployment

Hi everyone,

I've created a project called "Foundry OSD", and I would like feedback from people who deal with Intune or Autopilot in real environments.

Foundry OSD is an open-source Windows OS deployment toolkit built as a C# / WinUI 3 desktop app. It helps create ISO or USB deployment media, boot into WinPE, configure Ethernet or Wi-Fi networking, and prepare a machine before the rest of the provisioning flow.

This started as a personal project because I needed a simpler way to handle the steps that still happen around bare-metal prep and provisioning. I know there are already open-source options, but I personally wanted a 100% free and open-source tool that could be very simple to use while still allowing deep deployment customization when needed. I would like to see whether Foundry OSD can become useful beyond my own use case, so I am trying to collect practical feedback.

In practice, the workflow is:

  • automate ADK install/upgrade when needed
  • build ISO or USB deployment media
  • reuse cached Foundry OSD binaries, OS, and driver pack content on USB media across deployments
  • boot into WinPE
  • validate/select Ethernet or Wi-Fi networking
  • choose OS, driver pack, Autopilot profile, and deployment options from automated catalogs

After several months of work, it feels ready enough to show outside my own setup. Feedback from Intune and Autopilot admins is welcome, especially around real-world pre-provisioning and bare-metal scenarios.

Repo: https://github.com/foundry-osd/foundry

u/Mickael13880 — 15 hours ago
▲ 7 r/coolgithubprojects+1 crossposts

MeshGemma: offline disaster mesh for iPhone, phones find each other in airplane mode, gemma 4 runs on-device

me and a friend built this for a kaggle competition. no internet, no towers, no extra hardware. two phones in airplane mode find each other over bluetooth, sync signed incident reports across a multi-hop mesh, and run gemma 4 for medical Q&A and injury triage. open source.
https://www.kaggle.com/competitions/gemma-4-good-hackathon/writeups/new-writeup-1778607604484

youtube.com
u/Guus196 — 9 hours ago
▲ 22 r/coolgithubprojects+4 crossposts

Gilbert Codex v0.5.0 is the biggest update yet

Hey everyone, I just pushed Gilbert Codex v0.5.0, which is the biggest update I’ve put out so far.

Gilbert Codex is an open-source desktop AI workspace for coding, review, tools, research, image generation, and release work. It’s built with Tauri 2, React, TypeScript, and Rust, and the goal is to make AI coding feel more like a real desktop workspace instead of a bunch of disconnected tabs, terminals, chats, and setup screens.

GitHub / download:
https://github.com/UrbanWafflezz/GilbertCodex/releases/tag/v0.5.0

Repo:
https://github.com/UrbanWafflezz/GilbertCodex

This update moves the app way past the earlier alpha builds. v0.5.0 now has local user accounts, project-scoped chat state, pinned/searchable history, queued sends, planning/thinking controls, source-backed research, image-generation artifacts, safer review flows, subscription account routing, GitHub and Discord setup, and a much cleaner runtime under the hood.

A few of the bigger changes:

local users, so chats, projects, settings, and workspace state can stay separated

a more complete chat workspace with projects, attachments, markdown, regeneration, stop controls, and local persistence

multi-provider model routing for OpenRouter, OpenAI, Anthropic, Gemini, xAI, LM Studio, Ollama, Groq, Mistral, and DeepSeek

subscription account routes for Codex / ChatGPT, Claude Code, Gemini CLI / Cloud Code, GitHub Copilot, and more where supported

image generation that shows up as real chat artifacts with progress, grids, preview, and download controls

DuckDuckGo / Brave source cards for research-style work

local tools for files, terminal, browser preview, Git, GitHub, web search, and MCP-facing flows where permissions allow them

safer review gates for destructive actions, terminal/file work, publishing, credentials, and outside-scope paths

a refreshed README, release notes, installer docs, roadmap, support page, and public repo hygiene

I want to be clear that this is still alpha software. Windows x64 is the main packaged target right now, and the installer may show SmartScreen because it is not fully code-signed yet. macOS and Linux have source support, but I need people with those machines to test and help tighten them up.

The most useful feedback right now would be real-world testing: install issues, first-run setup problems, provider/model weirdness, confusing UI, bugs during real coding tasks, and anything around local files, terminal, Git/GitHub, sources, or image generation. If you open an issue, please include your OS, install/run steps, what you expected, what happened, and sanitized screenshots/logs if possible. Please do not include API keys, tokens, private repo content, local database files, or sensitive terminal output.

Also, since I’m being open about the project: if Gilbert Codex ends up being useful to you and you want to support the work, there’s a small support/funding page in the app and on the repo. No pressure at all, and nothing is locked behind it. The app is open source either way. It just helps me keep putting serious time into it, paying attention to bug reports, and making the project better instead of letting it become one of those half-finished tools that never gets maintained.

I’m trying to make Gilbert Codex into a genuinely useful open-source desktop coding-agent app, and v0.5.0 feels like the first version where the full shape of it is really starting to come together. If you try it, break it, review it, support it, or even just tell me what feels confusing, that would help a lot. App may feel slow on first boot!

u/Elegant_Associate889 — 10 hours ago
▲ 81 r/coolgithubprojects+2 crossposts

Typio: Make Your Terminal Type Like a Human

Typio is a lightweight Python library that prints text to the terminal as if it were being typed by a human. It supports multiple typing modes (character, word, line, sentence, typewriter, and adaptive), configurable delays and jitter for natural variation, and seamless integration with existing code via a simple function or a decorator. Typio is designed to be minimal, extensible, and safe, making it ideal for demos, CLIs, tutorials, and storytelling in the terminal.

Repo: https://github.com/sepandhaghighi/typio

u/sepandhaghighi — 17 hours ago

Built MagesticAI, a client-server APP for Spec-Driven Development with AI agents (open source, AGPL-3.0)

I got tired of watching LLMs "vibe-code" with no spec and no way to tell if the result actually matched what I wanted. I´ve wasted so much time reading and answering follow-ups questions, so I invested the last few months flipping the order: spec first, autonomous coding second.

That's MagesticAI: a self-hosted web platform where you describe what you want, the system turns it into a structured spec + technical plan, and then coordinated agents build it in isolated git worktrees.

The flow:

- Define project principles + rules

- Generate the spec (the "what" and the "why")

- Generate the technical plan

- Break into subtasks

- Implement with agents (in isolated worktrees so failures don't trash your repo)

- QA agent validates against the original spec

Stuff I think is interesting:

- Multi-LLM: Claude, Codex (GPT), Gemini, Ollama. Pick per phase.

- Ollama agentic mode with native tool calling (Read, Write, Edit, Bash, Glob, Grep)

works fully offline, no API fallback

- Built-in Kanban board, PTY terminal, Chat and Monaco editor in the browser

- Graphiti-based memory for cross-session context

- Three-track planning (Quick Flow / Standard / Enterprise)

- Docker compose for deployment

Tech stack: Python 3.12 / FastAPI, React 19 / Vite / Tailwind v4, Claude Agent SDK,

Graphiti + LadybugDB.

Honest limitations:

- Only tested on Ubuntu 24.04 + Docker; macOS/WSL2 should work but I haven't verified

- A few endpoint test suites are skipped (the routes exist on the roadmap but aren't

implemented yet, the tests stayed in the repo as a TODO)

- AGPL-3.0 . fine for personal/team use, the SaaS clause makes some companies

cautious.

Credit where due: MagesticAI is a fork of Aperant

(formerly Auto Claude Desktop) by AndyMik90.

I've added the Client-Server(Cloud), multi-LLM provider engine, Ollama native tools, BMad-style complexity-adaptive planning, and a bunch of UX cleanup.

Repo: https://github.com/dataseeek/MagesticAI

Genuinely interested in what people think and especially anyone who's triedagent-driven dev tools and bounced off them. What broke for you? Curious if SDD addresses any of those pain points.

u/Famous_Move_3591 — 16 hours ago

A link gatekeeper app

You give it a link to a website, it kinda encrypts/obfuscates it a bit, then you can send that new link to a friend. Before they get redirected to the original site, they have to solve a puzzle to get through.

The whole thing is entirely frontend, no backend at all. The link is basically stored inside the link itself lol

https://iwohost.github.io/HostLab/websites/gatekeeper.html

u/Its_Host — 20 hours ago
▲ 208 r/coolgithubprojects+7 crossposts

GitHub has a serious fake engagement problem and I wanted to see how visible it actually is through the public API, its worse than I thought after I went down that rabbit hole...

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars.

What I built

phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers):

  1. Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes
  2. Pulls star and fork events from the last 24 hours per repo
  3. Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history)
  4. Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%)
  5. Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window
  6. Files an issue directly on the targeted repo so the maintainer knows what's happening

Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended.

What the pattern actually looks like

It's remarkably consistent. A fake engagement campaign in the raw data:

  • 40-200 accounts, all created within the same 1-2 week window
  • Zero original repositories, or only forks they never touched
  • No bio, no location, no followers, no following
  • All of them starring the same repo within a 90-minute window
  • The target repo usually has a name implying it's a tool, hack, executor, or generator

Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast.

Notifying the affected repo

When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first.

Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently.

Why I built this

Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected.

It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/

The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users.

All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq.

Repo: https://github.com/tg12/phantomstars

Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process.

Questions welcome on the detection approach, GraphQL batching, or campaign ID stability.

github.com
u/SyntaxOfTheDamned — 1 day ago
▲ 21 r/coolgithubprojects+7 crossposts

VoidAccess v1.3, dark web OSINT platform, significant update

v1.0 extracted entities from Tor. v1.3 adds IP reputation

(Feodo/C2IntelFeeds/AbuseIPDB), GreyNoise scanner suppression,

domain pipeline (crt.sh cert transparency, URLScan, Wayback),

hash behavioral analysis via Hybrid Analysis, email breach history

via HIBP, paste sites, GitHub/GitLab scraping, 20 security RSS

feeds, CIRCL passive DNS, infrastructure cluster detection.

the STIX/MISP/Sigma exports were broken in v1.0 (empty bundles).

fixed in v1.1.

https://github.com/KatrielMoses/voidaccess

u/LockInternational893 — 20 hours ago

jscpd — Copy-paste detector for 223 programming languages, with CI integration, HTML reports, and an AI-optimized output mode

Copy-paste is one of the most common sources of technical debt, and jscpd is the most language-comprehensive tool I've found for hunting it down.

What it does

Finds duplicated code blocks across your codebase using the Rabin-Karp algorithm. You point it at a directory, it tells you exactly where you (or your teammates) copy-pasted.

npx jscpd ./src

That's it. No install required.

Why it stands out

  • 223 supported formats — JS, TS, Python, Go, Rust, Java, C/C++, PHP, Ruby, Vue, Svelte, Astro, Terraform, SQL, Markdown, YAML... even Brainfuck and APL
  • Cross-file detection — a &lt;script&gt; block in a .vue file can match a .ts file
  • CI-friendly--threshold 5 fails the build if duplication exceeds 5%
  • Multiple reportershtml, json, xml, sarif (GitHub Code Scanning), markdown, csv
  • AI reporter — compact output with ~79% fewer tokens, designed for piping into LLM prompts
  • MCP server — works as a Model Context Protocol tool for AI assistants
  • Ignore blocks — wrap noisy code with /* jscpd:ignore-start */ comments
  • Git blame integration — find out who wrote the duplicated blocks
  • Self-dogfoods — the repo runs jscpd on itself in CI

Sample output (silent mode)

Found 60 exact clones with 3414 (46.81%) duplicated lines in 100 files.
Execution Time: 1381ms

Links

u/Affectionate-Blood92 — 16 hours ago
▲ 2 r/coolgithubprojects+1 crossposts

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

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:

  1. Test your hypothesis in the notebooks/ folder.
  2. Implement it as a production-ready class in the strategies/ folder.
  3. Run python run_backtest.py to get an industry-standard tearsheet of your strategy's performance.
  4. 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!

u/cbrincoveanu — 18 hours ago