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Osamwonyi18/README.md

Hi, I'm Ken Nobak

Building FractalCycles, a platform for detecting statistically validated cycles in financial market data using signal processing and rescaled range analysis.

I started out in data analytics (Excel, SQL, Power BI) and kept pulling the thread until I landed in applied DSP and quantitative methods. FractalCycles is where that path ended up.

What I'm Building

FractalCycles applies classical signal processing to price data:

  • Goertzel DFT for spectral analysis of individual frequencies
  • Bartels test for statistical significance of detected cycles
  • Hurst exponent (R/S analysis) for regime classification (mean-reverting vs trending vs random walk)
  • Composite wave reconstruction for visualising the dominant cyclical structure

The platform runs across equities, FX, commodities, and crypto. Free tier available.

Open Source

  • hurst-calculator: standalone Python implementation of rescaled range analysis for estimating the Hurst exponent. Same methodology used inside FractalCycles, stripped down for clarity.

Technical Stack

Languages: Python, TypeScript, SQL Backend: Node.js, Fastify, PostgreSQL, Redis, Prisma Frontend: Next.js 14, React, Tailwind, TanStack Query Data: NumPy, signal processing (Goertzel, FFT, rescaled range) Infra: Docker, Railway, GitHub Actions

Earlier Data Analytics Work

Before FractalCycles, I completed a data analytics bootcamp with JustIT Training UK. Those projects are still pinned below if you want to see the progression.

Contact

Open to conversations about quant methods, signal processing applied to markets, or cycle detection generally.

Pinned Loading

  1. hurst-calculator hurst-calculator Public

    Rescaled Range (R/S) analysis for estimating the Hurst exponent. NumPy-only Python. Same methodology used inside FractalCycles.

    Python 1