Open to interesting problems$
MatthewBrulhardt
I work where machine learning meets markets and language. Reinforcement-learning trading agents on TensorTrade, aspect-based sentiment from raw reviews, and lately a probabilistic programming language written in Rust.
Last login - mwbrulhardt@portfolio
type "help" for commands, or try "ls"
$ gh api users/mwbrulhardt --jq stats
// the one I keep coming back to
schemepplRust
A probabilistic programming language built on a Scheme-like DSL, where inference is a language problem rather than a library call. Written in Rust, and the project I keep coming back to, with active commits through 2025.
$ ls ~/repos
Things I've shipped.
All public on GitHub, from reinforcement-learning trading agents to a probabilistic language in Rust. Open any one to read the source.
simple-sine-curve
34A tutorial for training a reinforcement-learning agent to trade on a simple sine curve, the clean signal you learn on before the noise of a real market. My most-starred repo.
penv
25An example for making highly customized environments in TensorTrade. Bend the trading environment to your strategy instead of fighting the framework.
- yelp-absaAspect-based sentiment analysis on the Yelp dataset. Not just whether a review is good or bad, but what the place is actually good at.Python 6
- canapiA universal client API generator. Describe the endpoints once and let the boring glue code write itself.Python 4
- financeAnalysis and prediction code on financial data. The notebook playground behind the trading work that followed.Jupyter Notebook 2
- feedA streamable version of pandas for online computation. Process data as it arrives instead of loading the whole frame at once.Python 1
// stack
What I build with
No vanity skill bars. Just the tools the repos are actually written in, grouped by where they live.
ML & Quant
Systems
Data & NLP
$ gh api users/mwbrulhardt/events
How the work moves.
A commit cadence that leans into project seasons, and a language mix that says more than a stack list: mostly Python, increasingly Rust.
commit cadence · illustrative overview
languages · public repos
- Python64%
- Rust18%
- Jupyter Notebook14%
- Other4%
// about
I build the agent,then the language it reasons in.
Most of my open source lives at the seam between machine learning and decision-making. I wrote the simple-sine-curve tutorial to show how a reinforcement-learning agent actually learns to trade on a clean signal, before the noise of a real market. Then came penv, for building TensorTrade environments you bend to a strategy instead of fighting the framework.
The other half is language. yelp-absa pulls aspect-based sentiment out of raw review text. Not just whether a review is good or bad, but what the place is good at. feed is a streamable take on pandas for online computation, and canapi generates API clients so the boring glue writes itself.
Right now I'm building schemeppl, a probabilistic programming language with a Scheme-like DSL written in Rust. It's the project I keep coming back to, where inference becomes a language problem instead of a library call.
An agent is only as good as the environment you let it learn in.
$ git log --oneline --reverse
Commit history.
2025 to now
schemeppl @ Rust · probabilistic programming
Building a probabilistic programming language on a Scheme-like DSL, treating inference as a language problem rather than a library call. The project I keep returning to.
RustScheme DSL2021
TensorTrade RL agents @ simple-sine-curve · penv
Wrote the tutorial that teaches an RL agent to trade a clean sine signal (34 stars), plus penv for building highly customized TensorTrade environments (25 stars).
PythonReinforcement LearningTensorTrade2021
NLP & streaming data @ yelp-absa · feed
Aspect-based sentiment analysis on the Yelp dataset, plus feed, a streamable version of pandas for online computation.
PythonNLPpandas2019 to 2020
Tooling & financial analysis @ canapi · finance
A universal client-API generator, plus a collection of analysis and prediction code on financial data that laid the groundwork for the trading work that followed.
PythonJupyterFinancial Data
// contact
Working on somethingin ML or markets?
Reinforcement learning, probabilistic inference, NLP, trading systems. If it lives near any of those, I'd like to hear about it. Everything I do is on GitHub.
Open to interesting problems · Long Island, NY