QuantLabsNet.com Public Quant Analytics Group

If you found the short overview useful but want something you can apply directly to your job search, I can produce a more focused, evidence-based deliverable tailored to PhD students, postdoctoral researchers, and experienced academics interested in quantitative finance.

Rather than providing another generic list of prestigious trading firms, the goal is to build a practical research and application package. Depending on what you need, this could be a firm-by-firm comparison, a briefing focused on a particular city or role, or a set of application materials designed specifically for quantitative research, trading, machine-learning, and high-performance systems positions.

Below are three possible deliverables, what each would contain, and how to choose the most useful one for your situation.

Short answer up front

 Major prop/HFT and quantitative trading firms that routinely hire PhDs include Renaissance Technologies, Two Sigma, D. E. Shaw, Jane Street, Citadel / Citadel Securities, Jump Trading, Hudson River Trading (HRT), DRW, IMC, Optiver, Flow Traders (and similar shops such as Virtu and Tower). Typical PhD roles are Quantitative Researcher, Quantitative Trader, ML/AI Research Scientist, Research Software Engineer, and Strategy/Execution Researcher. Frequently seen alma maters on public bios and LinkedIn include MIT, Stanford, Harvard, Princeton, Carnegie Mellon, UChicago, Columbia, UC Berkeley, Oxford, Cambridge, ETH Zurich and other top applied-math / physics / CS programs worldwide. Typical U.S. total-comp bands for PhD hires in 2025–26 that show up publicly and in community reporting are roughly $200k–$400k+ for junior-to-mid researcher roles (base + bonus/variation), with senior or “star” hires often materially higher. Read on for context, caveats, role breakdowns, representative evidence and pragmatic next steps if you’re thinking about…

The GitHub project bloomberg-terminal-app/bloomberg-terminal-free presents itself as an open-source, Python-based, institutional-style financial research terminal. Its pitch is ambitious: replicate much of the experience of a Bloomberg Terminal using free or open APIs, a terminal user interface, market-data aggregation, portfolio tracking, and AI-assisted analysis. For a solo quant trader, especially one already using Python, APIs, notebooks, local databases, and AI tools, the project is interesting—but it should be evaluated less as a “Bloomberg replacement” and more as a possible research interface, workflow wrapper, and idea-generation layer.

https://github.com/bloomberg-terminal-app/bloomberg-terminal-free

The key question is not whether this project can replace Bloomberg. For most serious market participants, it cannot. Bloomberg’s value is not only the terminal UI; it is its licensed data, historical depth, corporate actions, cross-asset coverage, pricing infrastructure, messaging network, analytics, fixed-income data, news, compliance features, and institutional reliability. A free GitHub project aggregating Yahoo Finance, CoinGecko, FRED, SEC EDGAR, Reddit, RSS feeds, and…

• 
  

  Hey everyone,

  We’re experimenting with a new way to package our algorithmic trading tools: single, self‑contained Python bots (think “all‑in‑one .py files” that you can drop into your own environment, no external dependencies, just run & trade).

• 
  

  Before we go any further, we’d love to know if there’s a market for this and what you’d prefer:

• 
  

  📊 Quick Poll

• 
  

  OptionVote✅ Yes – I would buy individually0%❌ No – not interested0%📅 I’d prefer a subscription for a new bot released daily0%

• 
  

  Click the poll to cast your vote!