Burst patterns shape demand on the Robinhood Chain blockchain network
Fuji: The Shape, Composition, and Spread of Demand Bursts on Robinhood Chain
Social and Information Networks
Summary
Blockchain marketplaces like Robinhood Chain often see sudden bursts of trading activity that cause demand for blockchain resources to spike. The authors studied these bursts by looking at detailed data from the platform's first three months. They found that bursts involve trading many different pools harder than usual, with demand rising and falling in a pattern that combines fast and slow changes. They also explored models explaining how these bursts start and grow, finding evidence for cascades triggered by outside events and some local trader interaction.
What this means in practice
- •For blockchain platform operators: Optimize resource allocation and congestion management by understanding burst demand patterns and their timescales on trading platforms.
- •For crypto trading system designers: Design trading bots and market systems that anticipate and adapt to bursts in user activity by modeling how demand peaks form and spread.
Tested on one dataset.
Authors
Pranay Anchuri
Abstract
Demand on blockchain-based marketplaces arrives in bursts and at a high rate. On these platforms the data are completely visible, which makes it feasible to compare the bursty demand against the usual load. The congestion manifests as an increased demand for gas, the primary resource a blockchain sells. Existing studies consider how to price the gas. The demand itself, and what it is made of during bursts, has not been measured. We measure it on Robinhood Chain, a layer-2 blockchain that quickly became a venue for trading tokenized equities and memecoins. We collected the data from every block of its first three active months and more than 50 million Uniswap swaps that occurred during bursty periods. We find that a burst trades about as many pools as a quiet period, mostly different ones, and trades them harder, on top of elevated demand that accumulated for hours. With the bursts aligned on their peaks, the median peak is 2.5 times the usual load. Both the rise and the fall of the demand are described by the sum of two exponential functions with different timescales, a slow one of hours and a fast one of minutes. Against a matched quiet period, a burst has 1.05 times the pools, 1.40 times the trades, and 1.21 times the senders. We use two models to explain the arrival of bursts: externally triggered cascades and self-excitation. The former fits the data relatively better. Inside a burst, the entry of each trader into a pool is followed for about 24 seconds by more entries than the existing traders predict. Whether this is because of imitation or because of a shared signal is left open.