Arati Uday Kamat

Independent Researcher

Algorithmic DEX trading · counterfactual analysis · post-rejection sampling methodology

Working papers

Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading: A Multi-Layer Intelligence Framework

Kamat, A. U. (2026) · arXiv submit/7684867 (in moderation) · SSRN abstract 6564803 · Manuscript DOI 10.5281/zenodo.19670719 · Dataset DOI 10.5281/zenodo.20043302 · In peer review at Ledger journal #632

Introduces a multi-layer intelligence framework for autonomous memecoin trading that incorporates hour-aware adaptive risk thresholds. Analyzes historical trade outcomes against UTC hour-of-day to identify high-variance time windows and dynamically adjust position entry, stop-loss, and filter strictness. Backtests on Solana DEX data demonstrate reduced drawdown relative to static-threshold baselines.

arXiv ↗ SSRN ↗ Manuscript DOI ↗ Dataset DOI ↗

Post-Rejection Follow-up Sampling: A Methodology for Counterfactual Outcome Measurement in Algorithmic DEX Trading

Kamat, A. U. (2026) · arXiv submit/7684836 (in moderation) · SSRN abstract 6607301 · Manuscript DOI 10.5281/zenodo.19671657 · Dataset DOI 10.5281/zenodo.20043516

Introduces Post-Rejection Follow-up Sampling (PRFS), a methodology for measuring what would have happened had a rejected trade been executed. By tracking forward price trajectories of rejected tokens over a fixed window, PRFS enables quantitative filter-quality evaluation. Applied to live Solana DEX trading data, the method identifies filters that reject profitable opportunities at higher rates than unprofitable ones, surfacing pruning candidates.

arXiv ↗ SSRN ↗ Manuscript DOI ↗ Dataset DOI ↗ Code (GitHub) ↗

Outcome-Classified Precision Auditing of Filter Rules in Algorithmic DEX Trading: Evidence from 2,400 Rejection Events

Kamat, A. U. (2026) · SSRN abstract 6638259 · Manuscript DOI 10.5281/zenodo.19720041 · Dataset DOI 10.5281/zenodo.19987697 · In peer review at Algorithmic Finance (ALG-26-0027)

Reports an at-scale empirical precision audit of eight filter rules operating in a live multi-strategy DEX trading fleet. Classifies approximately 2,400 unique rejection events over a fourteen-day operational window using an outcome schema extended from prior work (PRFS). Introduces the early-death classification: a rejected token's disappearance from the price oracle within sixty minutes is treated as an implicit positive save signal, justified by a sharply bimodal age distribution of single-sample events. Under this refinement, every active filter rule shows a net-positive precision verdict with an aggregate save-to-miss ratio of approximately fifteen to one; the conservative alternative (excluding early-death) yields approximately four to one.

SSRN ↗ Manuscript DOI ↗ Dataset DOI ↗

RED-2400: A Public Benchmark of Algorithmically-Rejected Trading Events with Outcome Labels

Kamat, A. U. (2026) · arXiv 2605.12151 [q-fin.TR] · SSRN abstract 6702198 · Multi-platform deposit: Zenodo + Kaggle + IEEE DataPort

First public benchmark dataset of algorithmically-rejected DEX trading events with linked post-rejection outcome trajectories. 6,659 rejection events, 169,122 outcome observations, 1,836 graveyard snapshots over the window 2026-04-10 to 2026-05-02 UTC. CC-BY-4.0 licensed. Deposited across five platforms (arXiv, SSRN, Zenodo, Kaggle, IEEE DataPort) for maximum discoverability and replication. RED-2400 is the first window in a planned dataset series; subsequent windows extend the time horizon and enable regime-stratified analysis.

arXiv ↗ SSRN ↗ Zenodo dataset DOI ↗ IEEE DataPort DOI ↗ Kaggle ↗ GitHub reader ↗

RED-2400 Replication Toolkit: A Python Package for Reproducible Filter-Precision Auditing

Kamat, A. U. (2026) · JOSS submission a440616519ce55b95631ec100c448318 · 18/18 byte-identical reproducibility tests passing

Python toolkit accompanying RED-2400. Packages the audit workflow as an installable library, includes a continuous-integration test suite that verifies the per-filter tables in the companion paper reproduce byte-identically against deposited reference outputs. License: MIT (toolkit) / CC-BY-4.0 (dataset).

JOSS submission ↗ GitHub ↗

Citing this work: each paper has a permanent DOI. GitHub repos have CITATION.cff files that generate BibTeX automatically via the "Cite this repository" button.

Code

red2400-replication-toolkit v1.0.0

JOSS submission · MIT License · Python · 18/18 byte-identical CI tests

Full replication harness for the RED-2400 benchmark. pip install red2400-toolkit; verifies all per-filter precision tables byte-identically against deposited reference outputs.

GitHub ↗ JOSS ↗ Software Heritage ↗

SWHID: swh:1:rev:a85bf1ff1eb4af345752555a3a1844e5b172bf3e (archived 2026-06-08)

post-rejection-sampling v1.0.0

Reference implementation of PRFS · MIT License · Python 3.9+

Clean reference implementation with RejectionTracker, FollowupSampler, and FilterEvaluator classes. Runs on synthetic data; no external infrastructure needed.

GitHub ↗ Software Heritage ↗

SWHID: swh:1:rev:f4571edd99cb1ef6cc41d826cf1b28ffec542bb8 (archived 2026-06-08)

red-2400-reader MIT

Lightweight reader for the RED-2400 dataset · Python

Minimal-dependency loader for the three RED-2400 files (rejections, outcomes, graveyard).

GitHub ↗ Software Heritage ↗

SWHID: swh:1:rev:d1be7e8b98133e6a3daa346cf249e455c6df5d61 (archived 2026-06-08)

Profiles

ORCID 0009-0000-4781-312X arXiv author page kamat_a_2 SSRN Author page (4 papers) Zenodo 9+ DOIs (papers + datasets + code) GitHub @aartikamat (4 public repos) Google Scholar Author profile Web of Science ResearcherID PYY-2705-2026 Academia.edu @AratiKamat2 IEEE DataPort RED-2400 dataset deposit Kaggle RED-2400 dataset deposit

Peer review service

Registered as a reviewer across the following programs and platforms:

Memberships and patents

Contact

Academic correspondence: arati.kamat@ieee.org. Research collaboration inquiries welcome via my ORCID profile.