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:2606.08232 · 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:2606.08228 · 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 ↗

Pump.fun Graduation Regime Windows: Survival Analysis of 832,941 Token Launches and the Social-Presence Effect

Kamat, A. U. (2026) · arXiv:2607.02823 · SSRN abstract 6915560 · Dataset DOI 10.5281/zenodo.20633486 · Dataset downloaded 525 times as of July 2026

Kaplan-Meier and Cox proportional-hazards survival analysis of 832,941 Solana pump.fun token launches with 24-hour graduation outcomes, observed continuously between 2026-05-08 and 2026-06-10. Pooled graduation rate 0.198 percent (Wilson 95 percent CI [0.189 percent, 0.208 percent]), a 3.18x decline from prior 2025 benchmarks. Social-channel presence exerts a large effect: launches advertising a Telegram channel graduate at 1.485 percent versus 0.166 percent without (8.94x lift, log-rank p less than 1e-300). Multivariate Cox concordance 0.858. Companion dataset RED-PUMP-2026-v1 released on Zenodo (CC-BY-4.0).

arXiv ↗ SSRN ↗ Zenodo dataset DOI ↗

Coordinated Sniper Cohorts on Pump.fun: Detection of 1,012 Persistent Wallet Rings and the Limits of Naive Causal Inference for First-Hour Buyer Flow

Kamat, A. U. (2026) · arXiv:2607.02795 · Dataset DOI 10.5281/zenodo.20978741 · Companion dataset RED-COHORT-2026 · USPTO Provisional 64/099,108 covers detection methodology

Public reproducibility-grade dataset of 1,012 persistent sniper-cohort detections on Solana pump.fun bonding-curve marketplace, derived from 1,578,333 buyer events across 166,098 token launches observed 2026-06-12 to 2026-06-26. Two-stage detection pipeline: co-occurrence graph across launches, edge weight >= 3, union-find connected components, scored by (10 * launches hit) + (5 / max(mean first-buyer rank, 1)) + sqrt(total SOL committed). Reports +132.3 percent lift in first-30-minute buyer count under 3:1 random-matched design, but the lift does not survive activity-matched placebo, demonstrating a launch-quality selection effect rather than coordination-specific causal effect. Interpretation-honest release of the detection catalogue enables downstream causal-identification research.

arXiv ↗ Zenodo dataset DOI ↗

Counterfactual Rejection Filter Ablation: An Empirical Application of Post-Rejection Follow-up Sampling to Solana Memecoin Trading

Kamat, A. U. (2026) · SSRN preliminary upload 7045818 (submitted 2026-07-03) · Dataset DOI 10.5281/zenodo.21149176 · Extends PRFS methodology (Paper 2)

Applies the Post-Rejection Follow-up Sampling framework to eight active filter rules operating on a live Solana DEX trading fleet. Systematic ablation shows every filter rejects a median-loser distribution, validating the aggregate save-to-miss ratios reported in prior work. Documents 158 counterfactual >10x winners across the observation window, with dominant reject-reason failure modes identified for downtrend_24h, marketcap_out_of_range, and multi_tf_fade rules. Naive outcome measurement systematically inverts these findings, motivating counterfactual-first evaluation for algorithmic decision systems.

SSRN ↗ Zenodo dataset DOI ↗

A Multi-Venue Solana/DeFi Microstructure Data Corpus: The RED-2400 Family v2

Kamat, A. U. (2026) · SSRN preliminary upload 7062579 (submitted 2026-07-05) · Corpus dataset paper covering five deposited datasets: RED-ORACLE, RED-ARB, RED-LIQ, RED-BASIS, RED-BRIDGE

Corpus-level dataset paper describing the RED-2400 family v2 of Solana / DeFi microstructure benchmarks. Five deposited datasets on Zenodo under CC-BY-4.0: RED-ORACLE-2026-v1 (Pyth oracle staleness for BTC/ETH/SOL), RED-ARB-2026-v1 (328,186 Solana CEX-vs-DEX spread observations across 32 DEX venues), RED-LIQ-2026-v1 (18,750 Ethereum Aave V3 liquidations + 4-chain lending-utilization panel), RED-BASIS-2026-v1 (BTC/ETH/SOL spot-perpetual basis on OKX), RED-BRIDGE-2026-v1 (360,714 Wormhole cross-chain messages). All five span a common 57.4-day 2026 window (2026-05-08 to 2026-07-05). Standardized schema conventions, cross-dataset lineage, and reproducibility manifests for external researchers extending the methodology.

SSRN ↗ RED-ORACLE Zenodo ↗ RED-ARB Zenodo ↗ RED-LIQ Zenodo ↗ RED-BASIS Zenodo ↗ RED-BRIDGE Zenodo ↗

Sniper Cohorts and Algorithmic Filter Rejections in Solana Memecoin Markets: Two-Window Replication of Lifecycle-Stage Population Separation

Kamat, A. U. (2026) · SSRN abstract 7128818 · Dataset DOI 10.5281/zenodo.21399918 · Companion dataset LIFECYCLE-SEPARATION-2026-v1

Two independent algorithmic detection streams applied to the same Solana memecoin market during the same observation windows produce nearly disjoint token-set outputs. In the June 2026 window, 2 of 20,162 sniper-cohort-detected mints (0.010 percent) appear in the algorithmic rejection stream. In the July 2026 window, 5 of 623 cohort-detected mints (0.803 percent) appear. Cross-window cohort overlap between v1 and v2 is zero mints. The separation is structural: cohort detection operates at the pump.fun bonding-curve stage, while rejection filtering operates on PumpSwap and other post-graduation venues. Four alternative explanations (time-window artifact, data-source mismatch, DEX venue asymmetry, pipeline-version artifact) are systematically ruled out.

SSRN ↗ Zenodo dataset DOI ↗

RED-REJECT-2026-v1: A 96-Day Public Corpus of Algorithmic Filter Rejections with Post-Rejection Follow-up Samples on the Solana Pump.fun Ecosystem

Kamat, A. U. (2026) · SSRN abstract 7129798 · Dataset DOI 10.5281/zenodo.21402476 · 716,762 sample records across 4,635 unique SPL token addresses

Public benchmark corpus of algorithmic filter rejections with post-rejection follow-up samples, collected from a single blockchain observer on the Solana pump.fun ecosystem between 2026-04-11 and 2026-07-16 (96 days continuous, 13.7 weeks). Corpus size: 716,762 post-rejection sample records across 4,635 unique SPL token addresses. Each row represents a scheduled follow-up observation of a token rejected by one of eight algorithmic trading filter families (downtrend_24h 56.11 percent, marketcap_out_of_range 21.37 percent, multi_tf_fade 11.98 percent, timeSlot_not_strong 5.17 percent, hour_blacklist 2.35 percent, ultra_fast_reject_exit 1.64 percent, utc_hour_blocked 0.91 percent, h6_sellers_dominating 0.41 percent). DEX venue coverage: pumpswap 91.79 percent, meteora 2.73 percent, raydium 2.46 percent, pumpfun bonding-curve 1.83 percent. Operationalises the Post-Rejection Follow-up Sampling (PRFS) methodology at production scale.

SSRN ↗ Zenodo dataset DOI ↗

Dev-Wallet Silence Is Not a Rug Predictor on Solana Pump.fun: Empirical Evidence from a Positive-Class and Control-Panel Study

Kamat, A. U. (2026) · Concept DOI 10.5281/zenodo.21434629 · Version 1.0 DOI 10.5281/zenodo.21434630 · Positive class n=52 rug events, control class n=91 non-rug wallets

Null-result empirical study testing whether dev-wallet transaction silence (>=6 hours) in the 24-hour window before a rug distinguishes rug tokens from non-rug tokens. Positive class (n=52 rug events) shows 96.15 percent silence rate (95 percent Wilson CI [87.0, 98.9]); control class (n=91 non-rug wallets) shows 98.90 percent silence rate (95 percent Wilson CI [94.0, 99.8]). Two-proportion z-test z=-1.10, p=0.27; Fisher's exact test p=0.30. Balanced accuracy 48.62 percent; Youden J index -2.75 percentage points. The silence signal fails as a rug predictor because non-rug dev wallets are also mostly silent in random 24-hour windows post-graduation. Finding is robust across silence-threshold sensitivity sweeps of {2, 4, 6, 12, 18, 24} hours. Deposited as SILENT-KILLER-2026-v1 reproducibility bundle. Target venue: Cryptoeconomic Systems (MIT Press).

Zenodo v1.0 ↗ Zenodo concept ↗

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_1 SSRN 5 distributed + 4 in editorial review (top 15.2% author) Zenodo 20 DOIs (papers + datasets + code) GitHub @aartikamat 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

Five completed DOI-anchored peer reviews across three scholarly publishing portals:

Registered as a reviewer across the following programs and platforms:

Public writing

The Solana Memecoin Success Rate Fell 3x. The Story Isn't What You Think. (July 15, 2026)

Why the headline pump.fun graduation rate is misleading, and what a Cox model actually shows about 832,941 token launches. Cross-published on Medium and Substack.

Medium ↗ Substack ↗

Memberships, patents, and trademarks

Professional memberships:

USPTO patents and trademarks (Micro Entity status):

Contact

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