cryptocurrency traders

Abstract: We study coordinated groups of wallets on a bonding-curve launchpad (pump.fun on Solana), using a complete record of 200\,443 token launches, 15\,854\,121 curve trades and 51\,025\,958 SOL transfers over 88.3 hours. A prospective, multi-channel discovery procedure (co-buying, shared funding, joint signing, transfers, consolidation) yields 23\,710 wallet groups. Without ground-truth labels we validate them by out-of-sample link prediction, independent non-trading links and a comparison with naive co-buy clustering at equal numbers of flagged tokens: raw clustering collapses into one giant component, yet it ranks tokens at least as well as the groups, so the value of the structure is descriptive. Group activity is highly concentrated and splits into serial creators and long-lived rings of early buyers. Known members buy earlier than other buyers of the same token and extract more cash than an activity-matched placebo set, while late buyers' net cash flow is negative; tokens with members show a pump-like price path, but the contrast vanishes when controls are restricted to comparable tokens, so we report it as an association. A follower entering at the earliest group signal loses money out of sample in all 32 configurations and does no better than random entry. A few pools of tens to over a hundred fast wallets are associated with a two- to four-fold higher chance of reaching 20 SOL, also within creators, with a smaller excess at 60 SOL and none for graduation; on a final hold-out the team rules keep a precision of about one half to three quarters, yet many signals arrive when the curve already holds 20 SOL, one team supplies most of them, and they add no information beyond early curve state. We also document evaluation errors that turn the same data into apparent profit.

Abstract: The cryptocurrency ecosystem has experienced extraordinary growth alongside an equally remarkable rate of failure, with over 52 percent of all tokens launched since 2021 ceasing to trade by early 2025. Despite the scale of this phenomenon, predictive modeling of cryptocurrency death remains an underdeveloped area of research, constrained by definitional ambiguity, data scarcity, and the absence of granular lifecycle frameworks. This work investigates whether the failure of cryptocurrency assets can be predicted using publicly available market data and deep learning methods. A Long Short-Term Memory (LSTM) recurrent neural network was trained on 90-day sequences of daily reference price and estimated market capitalization for 82 cryptocurrency assets (41 alive and 41 dead), sourced from Coin Metrics over the period 2020 to 2026. The model was evaluated using a strictly chronological train-test split to prevent look-ahead bias. The LSTM classifier achieved in best cases a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.98 on the held-out test set. Finally, the model was applied to unseen data, and it was observed that the ROC AUC decreased between 0.59 and 0.65. The findings demonstrate that temporal patterns in price and market capitalization alone contain sufficient discriminative signal to identify assets on a trajectory towards economic inactivity. Diagnostic analyzes reveal that failing assets exhibit gradual value erosion and elevated volatility in the months preceding inactivity, rather than sudden catastrophic collapse. This work also documents the practical infeasibility of a multi-stage lifecycle model under current data conditions and justifies the transition to a binary classification approach.

Abstract: The liquidity providers of most typical automated market makers (AMMs) are passive and known to suffer from adverse selection. AMMs traditionally rely on trades executed on them to sync the price with external markets (and a notion of fair value thereof). As a result, when an external fair value moves, arbitrageurs trade on AMMs to pick off their stale quotes. A recent approach termed proprietary automated market makers (propAMMs) emerged as a response in 2024: these on-chain programs instead have their singular operator quote from its own inventory, repricing without a trade via efficiently provided price updates. By 2026, propAMMs accounted for more than half of SOL/USDC volume on Solana. We present the first year-long longitudinal measurement of propAMMs on Solana, Base and Monad, and decode the on-chain logic of the dominant propAMM on Base. We find that two seconds after a fill, propAMMs earn 0.37 bps on Solana and 1.19 bps on Base, while AMMs lose 0.22 and 0.62 bps. We empirically quantify and classify their edge as stemming from four factors: propAMMs continuously reprice rather than waiting for a trade, they charge for the source-dependent risk each counterparty might bring, they avoid cross-venue arbitrage from other on-chain markets (such as other AMMs), and they spoof by filling trades at a worse price than they quote. Finally, we show that on our proxy for retail flow (i.e., fills that arrive when the reference price is not moving) propAMMs collect 0.26 bps on Solana where AMMs collect 2.59 bps, indicating that they offer better prices for short-term uninformed flow.

Abstract: We study cross-chain arbitrage when autonomous AI agents, rather than humans or bots, are the searchers. We model agents as both arbitrage extractors and Maximal Extractable Value targets, derive the optimal trade size for a risk-averse agent under mean-variance utility with stochastic bridge delays, and formalize multi-chain path selection as a belief-weighted online learning problem whose belief estimates converge under a Robbins-Monro schedule. Using 23,000 Uniswap V3 swap events across Ethereum, Arbitrum, and Base, we find that Ethereum-Arbitrum price gaps average 0.044% at 10-second resolution and Arbitrum--Base gaps average 0.013%, so $10,000 trades clear in 63% of L2-L2 windows via CCTP while L1-L2 routes require $50,000 or more for comparable viability. Our adaptive path-selection algorithm outperforms standard baselines by 11% on average, and moderate randomization cuts MEV exposure by over 50% with only modest profit loss.

Abstract: Once complex, creating and deploying a new cryptocurrency has become trivial. Coin launchpads now allow users to generate a new coin with merely a few clicks, at a minimal cost. Launchpad popularity has grown in tandem with the rise of "meme coins," which usually do not offer any novel technological properties and are purely created for fun. The most prominent coin launchpad, pump.fun, has gained significant traction, grossing over 100 million USD in daily trading volume. The mass adoption of coin launchpads, however, also enables strategic actors to easily manipulate trading signals, unbeknownst to inexperienced traders who then buy certain coins, and enable these strategic actors to profit from rapid and unsustainable price increases ("pumps"). To identify such manipulations at scale, we conduct a large-scale study of pump.fun, collecting information on all 15 million coins launched in the last two years, and performing analysis on large, random samples of transaction data. We identify five classes of manipulation strategies: 1) wash trading, 2) creator address obfuscation, 3) coordinated sell, 4) copycat coins, and 5) social media manipulation. We find that strategic actors often bypass the platform interface and implement these strategies in a highly automated and low-latency fashion, by interacting directly with the blockchain. We further uncover the existence of "Market-Manipulation-as-a-service (MMaaS)," third-party tools that enable users to perform these manipulations without any technical expertise. We conclude by devising mitigations and proposing recommendations for traders, pump.fun, wallets or chain scanners, software development platforms, and regulators.