About
Original, empirical work on quantitative methods, AI, and financial market dynamics.
A personal initiative. Applied Markets publishes original, empirical work on quantitative methods, AI, and financial market dynamics. Every artifact begins with a question that has a tractable experimental form.
Each piece of work starts with a specific, scoped question — one that has a tractable experimental form. The output is a traceable record of method, data, and result. Partial findings are published. Failed experiments are published. The record is the product.
Artifacts are released as experiments, articles, and Python notebooks. Every release comes from direct implementation and first-hand measurement — not summaries of papers, not passive study notes.
Research Pillars
Quantitative Finance
The mathematical foundations that markets run on. Statistics, probability, information theory, and stochastic processes — studied through original derivations and empirical analysis. The goal is not to reproduce textbook results but to test where the standard assumptions hold and where they break.
AI for Finance
Modern AI applied to financial reasoning, forecasting, and market simulation. LLMs, reinforcement learning, and agent-based models — tested on real data with honest results. The question driving each experiment is whether the AI method is genuinely useful or just appears to be.
Why These Questions
Still open
These are not settled questions with known answers. The experiments exist because the results are not already obvious.
Scoped
Each question is specific enough that a single focused experiment can produce real evidence, not just suggestive patterns.
Data-driven
Theory informs the setup, but the data decides. Hypotheses are stated before experiments run, not after results arrive.
Accumulates
Each experiment builds on prior ones. A running record of what has been tested and what it showed is more valuable than isolated results.
What Gets Published
Reproducible
Every result comes with enough method detail to reconstruct. Data sources, parameters, and decision points are explicit.
Open code
Notebooks and code are published alongside results. If it can't be reproduced, it's a note — not a release.
Including negatives
Experiments that don't confirm the hypothesis are published. A null result is still a result, and often the more informative one.
Original only
No summaries of other people's work. No passive learning notes dressed as research. Original implementation and first-hand observation only.
Editorial Boundary
- No trading signals Published work is research, not strategy. No alpha, no predictions meant to trade on.
- No learning notes Published work should not be passive study notes.
- No reposting Do not summarize other people's blogs or papers as output.
- Original only Keep the record tied to direct implementation and measurement.
- Keep moving Progress matters more than chasing state-of-the-art optics.
Operating Loop
Pick
Pick one market phenomenon, instrument, or mechanism to study.
Set
Set one hypothesis and the key questions around it.
Execute
Execute the smallest real experiment that produces evidence.
Record
Record the result, even if it is partial or failed.
Behind the Work
Applied Markets is a personal initiative, maintained alongside Prachalabs.com and Pracha.me. A public record of ongoing research — not a consulting practice, not a publication venue, and not a trading operation.
Every release comes from original experiments, direct implementation, and first-hand observation. Not summaries of papers, not passive learning notes.