AI trading signals for Ethereum are in focus as the network approaches its Fusaka Upgrade. According to the source, analysts see AI-driven research helping traders time entries and assess risk while the market reacts to mixed flows.
Fusaka sets ambitious performance targets
The Ethereum “Fusaka Upgrade” is officially slated for mainnet on December 3, 2025. It will introduce Peer Data Availability Sampling (PeerDAS) to expand data capacity up to 10 times. As a result, developers are targeting an estimated 12,000 TPS across the ecosystem by 2026.
That scalability roadmap, according to the source, is a key narrative for ETH utility. It may shape expectations into next year, especially as infrastructure teams prepare for higher throughput.
Whales sell while ETFs buy
Meanwhile, large Ethereum whale wallets offloaded about 90,000 ETH in the past 48 hours, worth nearly half a billion dollars. The sales coincided with a 6.53% price drop, likely due to profit-taking, per the source.
Conversely, institutional Ethereum ETF purchases totaled $213.1 million on September 18. Notably, this surpassed Bitcoin ETF purchases that day, indicating strong interest in ETH’s utility, according to the source.
How AI research fits the plan
Recent analysis argues that LLMs can pinpoint timing and risk factors by studying past trades. More importantly, they can process vast data to detect patterns, sentiment shifts, and project fundamentals. Therefore, an AI trading signals for Ethereum approach could help synthesize whale activity, ETF flows, and upgrade milestones into a coherent strategy.
Key inputs AI models might track include:
On-chain whale distribution trends
ETF net flows versus price action
Fusaka development milestones and test reports
For foundational context on value transfer and on-chain liquidity, see our guide to how stablecoins work. For background on ETFs and market structure, refer to this ETF overview.
