---
title: "Open-Source Repos from Quant Funds and Banks"
date: 2026-05-21
source: agent research
---

# Open-Source from Quant Funds and Banks

106 repositories cataloged across 9 major firms. Sorted by practical utility for a solo systematic trader running VectorBT + NautilusTrader + TimescaleDB.

## Tier 1: Immediately Useful

| Repo | Stars | Language | License | What It Does |
|---|---|---|---|---|
| [man-group/ArcticDB](https://github.com/man-group/ArcticDB) | 2,320 | C++/Python | BSL (free non-commercial) | Serverless time-series database. Billions of rows in S3. Pandas in, pandas out. Bloomberg licensed this. |
| [man-group/dtale](https://github.com/man-group/dtale) | 5,144 | Python | Apache 2.0 | Interactive pandas DataFrame explorer in browser. One line of code. Filters, correlations, charts. |
| [goldmansachs/gs-quant](https://github.com/goldmansachs/gs-quant) | 10,358 | Python | Apache 2.0 | Quantitative finance toolkit. Market data, risk models, analytics. Most complete firm-backed trading library. |
| [man-group/notebooker](https://github.com/man-group/notebooker) | 900 | Python | Apache 2.0 | Turns Jupyter into scheduled reporting engine. Daily analytics runs. |
| [deshaw/pyflyby](https://github.com/deshaw/pyflyby) | 409 | Python | BSD | Auto-import for IPython/Jupyter. Saves hours across 50 notebooks/day. |

## Tier 2: Useful for Specific Workflows

| Repo | Stars | Language | License | What It Does | When to Use |
|---|---|---|---|---|---|
| [man-group/PyBloqs](https://github.com/man-group/PyBloqs) | 183 | Python | Apache 2.0 | HTML reports from Python. Tables, charts, layout in blocks. | Generating tearsheets/reports for review. |
| [deshaw/versioned-hdf5](https://github.com/deshaw/versioned-hdf5) | 89 | Python | BSD | Version control for HDF5 files (git for scientific data). | Rolling back to "yesterday's version" of a dataset. |
| [deshaw/nbstripout-fast](https://github.com/deshaw/nbstripout-fast) | 29 | Rust | BSD | Strips outputs from Jupyter notebooks before git commits. Fast. | Git hygiene for research notebooks. |
| [optiver/timestamp9](https://github.com/optiver/timestamp9) | 65 | Python | Apache 2.0 | Nanosecond timestamps for Python. | When tick data needs sub-millisecond precision. |
| [optiver/optiver-asyncpg](https://github.com/optiver/optiver-asyncpg) | N/A | Python | Apache 2.0 | Production fork of asyncpg (fast Postgres client for asyncio). | Writing ticks at latencies psycopg2 can't reach. |
| [yli188/WorldQuant_alpha101_code](https://github.com/yli188/WorldQuant_alpha101_code) | 748 | Python | MIT | Community implementation of all 101 alpha formulas from WorldQuant's "101 Formulaic Alphas" paper. | Factor research starting point. |

## Tier 3: Interesting but Not Directly Actionable

### Two Sigma

| Repo | Stars | Language | What It Does | Why Not Now |
|---|---|---|---|---|
| [twosigma/beakerx](https://github.com/twosigma/beakerx) | 2,800 | Java/Python | Multi-language Jupyter (Python, Scala, Groovy, Kotlin, Clojure, Java). | Standard Jupyter suffices. |
| [twosigma/flint](https://github.com/twosigma/flint) | 1,000 | Scala | Time-series joins on Apache Spark with temporal tolerance. | Overkill; DuckDB handles your scale. |
| [twosigma/cook](https://github.com/twosigma/cook) | 338 | Clojure | Batch job scheduler on Mesos/Kubernetes. | systemd is fine at single-server scale. |
| [twosigma/marbles](https://github.com/twosigma/marbles) | 116 | Python | Unit tests that explain failures in plain English. | Nice-to-have, not critical path. |
| [twosigma/frost](https://github.com/twosigma/frost) | N/A | SystemVerilog | FPGA RISC-V core. | FPGA hardware design; not relevant to retail. |

### Jane Street (OCaml Ecosystem)

| Repo | Stars | What It Does | Why Not Now |
|---|---|---|---|
| [janestreet/core](https://github.com/janestreet/core) | 1,200 | Alternative OCaml standard library. | OCaml-only. Not useful for Python stack. |
| [janestreet/magic-trace](https://github.com/janestreet/magic-trace) | 5,300 | High-resolution process tracer via Intel Processor Trace. | Useful if you ever profile at CPU-instruction level. |
| [janestreet/async](https://github.com/janestreet/async) | 233 | Cooperative concurrency in OCaml. | OCaml-only. |
| [janestreet/hardcaml](https://github.com/janestreet/hardcaml) | 1,000 | OCaml library for hardware design (FPGA, ASIC). | FPGA design; academic interest only. |

### Hudson River Trading

| Repo | Stars | What It Does | Why Not Now |
|---|---|---|---|
| [hudson-trading/corral](https://github.com/hudson-trading/corral) | 175 | Structured concurrency for C++20. | C++ infrastructure; not relevant to Python stack. |
| [hudson-trading/slang-server](https://github.com/hudson-trading/slang-server) | 224 | SystemVerilog language server. | FPGA tooling. |
| [hudson-trading/heracles-ql](https://github.com/hudson-trading/heracles-ql) | 27 | Python DSL for alerts. | Too small; custom Prometheus alerts are better. |

### D.E. Shaw

| Repo | Stars | What It Does | Why Not Now |
|---|---|---|---|
| [deshaw/pjrmi](https://github.com/deshaw/pjrmi) | 47 | RPC between Python and Java. | No Java in your stack. |

## Banks

### Goldman Sachs
- [goldmansachs/gs-quant](https://github.com/goldmansachs/gs-quant) (10,358 stars) -- **Tier 1**. Full quantitative finance toolkit.

### JPMorgan Chase
- [jpmorganchase/python-training](https://github.com/jpmorganchase/python-training) (4,000 stars) -- Internal Python training materials. Reveals how a bank teaches quant Python.
- [jpmorganchase/quorum](https://github.com/jpmorganchase/quorum) -- Enterprise Ethereum fork. Blockchain-focused.

### Morgan Stanley
- No significant public repositories for trading.

## The Silent Ones (Zero Public Repos)

| Firm | AUM | Status |
|---|---|---|
| Renaissance Technologies | ~$130B | Zero. NDAs prevent even detailed resumes. |
| Citadel / Citadel Securities | ~$65B | No official GitHub account. |
| Bridgewater Associates | ~$100B | Zero. |
| Millennium Management | ~$70B | Zero. |
| Point72 / Cubist Systems | ~$35B | Zero. |
| AQR Capital | ~$99B | Has an account; contains only a pandas fork. Effectively nothing. |

## Recommended Stack from Fund OSS

```
Data Storage:     ArcticDB (serverless, Man Group, optimized for time-series)
Data Exploration: dtale (Man Group, interactive DataFrames)
Analytics:        gs-quant (Goldman Sachs, risk models + market data)
Reporting:        notebooker (Man Group, scheduled Jupyter reports)
Factor Research:  WorldQuant alpha101 (community, 101 formulaic alphas)
Jupyter DX:       pyflyby (D.E. Shaw, auto-imports)
Git Hygiene:      nbstripout-fast (D.E. Shaw, Rust, fast notebook stripping)
```

## Why Some Firms Share and Others Don't

Three patterns:

1. **Recruiting brand** (Two Sigma, D.E. Shaw, Jane Street, HRT): Public code attracts PhDs. magic-trace's 5,300 stars = every infrastructure engineer now knows where to send a CV.

2. **Marketing after IP is already licensed** (Man Group): Bloomberg already paid for ArcticDB. The rest is marketing for a publicly listed company.

3. **Any public code = hint to competitors** (Renaissance, Citadel, Bridgewater): Renaissance doesn't even allow former employees to write detailed resumes about their work.

**For solo traders**: The "recruiting brand" firms produce the most useful tools because they're designed to be general-purpose infrastructure, not alpha-generating code. Alpha stays private everywhere.