Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.00005

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

Arati Uday Kamat
View original source ↗Read source document ↗Export metadata ↓

Abstract

Empirical research on decentralized and Solana-native market microstructure is constrained less by method than by data: the strongest results in the field rest on a small number of Ethereum-centric datasets, and public, record-level tapes of Solana venues, cross-chain flows, and on-chain oracle behavior remain scarce. We release and document the RED-2400 family v2, a corpus of five public benchmark datasets assembled by a single public-blockchain observer over a concurrent 57-day 2026 window (2026-05-08 to 2026-07-05) under one methodology (free-tier and public feeds only, no paid data, no private accounts, no trading), each with fixed-seed reproducibility scripts, SHA-256 manifests, and a CC-BY-4.0 licence. The five datasets characterize, respectively, Pyth oracle staleness against centralized-exchange prices (164,002 observations), an Aave utilization and Ethereum liquidation panel (18,750 liquidations plus a four-chain utilization series), spot-perpetual basis and funding coupling (244,719 observations), a Solana CEX-versus-DEX spread tape with realized impact (328,186 observations), and a Wormhole cross-chain source-flow tape (360,714 messages). Across the corpus we report three descriptive findings, one honest null, and one field-completeness characterization; none is presented as a causal estimate. The contribution is a coherent, reproducible benchmark family that complements the Ethereum canon and lowers the data barrier for Solana and cross-chain microstructure research.

Explore related subjects

q-fin.TRq-fin.CPq-fin.ST

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arati Uday Kamat. 2026-07-09. A Multi-Venue Solana/DeFi Microstructure Data Corpus: The RED-2400 Family v2. https://arxiv.org/abs/2610.00005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Combinatorial Arbitrage in Prediction Markets

We develop a framework to identify and quantify arbitrage across logically related prediction-market contracts. We prove that prices outside the convex hull of admissible payoff vectors admit arbitrage and identify optimal arbitrage portfolios through integer programming. Using venue metadata and semantic analysis of resolution criteria, we recover 1.9 million logical relations and compile them into constraint systems covering more than 255,000 contracts. We replay full-depth Polymarket order books to simulate an arbitrageur executing these portfolios against resting orders. Among settled portfolios, an ideal arbitrageur that takes the full displayed depth of every opportunity and reuses its capital at resolution earns a risk-free return of 3.5% a week after spreads and fees. We find that arbitrage opportunities persist longer and attract less observed participation where relation discovery and execution are more costly.

q-fin.TR↗
arXiv2026

Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books

Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding their inner workings, research directions are hard to narrow and practical use in real-world trading environments remains uncertain. This study therefore asks what these models actually learn about market microstructure. We find that, similarly to games where sequence models build an internal representation of the board, an 8.2M-parameter Mamba-3 state-space model trained autoregressively on the tokenized message stream of eight large-cap US equities learns an internal representation of the market and its dynamics. In particular, order-flow imbalance, a central driver of short-horizon price moves, is decodable at $R^2 \approx 0.59$, far above controls and baselines. This representation extends to unseen stocks, suggesting that these models learn general representations rather than ticker-specific patterns. The model also generalizes across metrics beyond order-flow imbalance: results hold across traded volume and volatility. Additionally, the representation is causally steerable: pushing the state along a feature's axis shifts the model's next-message beliefs as that feature implies, monotonically in dose, consistent with the emergence of a world model.

q-fin.TR↗
arXiv2026

Topological Compression of Prediction Market Belief States for Event Forecasting

Topological Data Analysis (TDA) and Persistent Homology (PH) have been utilized in numerous fields$-$from protein modeling to financial analysis$-$to understand the underlying structure of data. In this paper, we incorporate TDA and PH into forecasting models over a prediction market universe to determine whether the belief state of such markets forms an underlying structure whose shape can provide predictive insight. We conclude that single-parameter PH is able to improve predictive ability and calibrated accuracy of linear forecasting models, while two-parameter multipersistence produces similar benefits for nonlinear models.

q-fin.TR↗