About
About Nyctron
An independent engineering and research project exploring quantitative finance, algorithmic trading, computational infrastructure, and AI-assisted research.
About Nyctron
Nyctron is an independent engineering and research project. Its work sits at the intersection of quantitative finance, algorithmic trading, computational infrastructure, and AI-assisted research, with an emphasis on practical tools and reproducible methods.
The project is developing incrementally. Work is organised around questions that can be investigated with clear methods, documented assumptions, and results that others can inspect. Nothing on this site should be read as a claim that a product has launched, that research results have been achieved, or that capabilities exist beyond what is described.
Origin of the name
The name Nyctron is invented. It was inspired by Nyctosaurus, an extinct genus of pterosaur — a flying reptile that lived alongside the dinosaurs but is not classified as one.
The reference is a matter of identity and tone rather than a scientific claim. It is not intended to imply any technical, historical, or biological connection to the animal.
People
Nyctron is currently a small independent effort. The profiles below introduce the project's co-founders.
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Mohammad Rajabi
Co-founder
Studies Computer Engineering, with a background in trading and quant. Works across Python, AI/ML, and Linux. Originated Nyctron and leads quant, infrastructure, ML, and algorithms.
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Mehdi Sadeghian
Co-founder
Studies Computer Engineering. Works in Python and trades crypto; focuses on algorithmic trading and AI/ML.
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Aria Fatahi
Co-founder
Studies Financial Management. Leads research and financial work, including strategy ideation and evaluation.
Areas of work
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Quantitative research
Methods, strategy evaluation, time-series analysis, and risk assessment, with assumptions stated explicitly.
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Computational infrastructure
Software and Linux-based environments that make quantitative workflows repeatable and easier to operate.
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AI-assisted tools
Practical uses of artificial intelligence in research and engineering workflows, evaluated rather than assumed.
How we work
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Reproducibility
Methods, assumptions, and results should be inspectable and reproducible by someone else.
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Practical engineering
Tools are built to solve concrete problems, not to demonstrate technology for its own sake.
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Critical evaluation
Claims are tested and questioned, including our own, before they are repeated.
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Honest documentation
Limitations and open questions are documented alongside results and assumptions.
Contact and collaboration
Nyctron can be reached through the channels below. The GitHub organization is public; a team LinkedIn page and a professional email address will be added here once they are available.
- GitHub github.com/nyctron
- Email Soon
- LinkedIn Soon