A prominent ETH whale at a high position further increased their holdings by averaging their cost, purchasing an additional 2732 ETH.
BlockBeats News, December 3rd, according to on-chain analyst Ai Auntie (@ai_9684xtpa), a whale who had previously opened a position on September 25th with an average price of $4026 for 736 ETH ($2.96 million) sought to lower their cost basis. On December 2nd to 3rd, they further averaged down by purchasing 2732 ETH at an average price of $2988, investing an additional $8.16 million.
This whale has now invested a total of $11.128 million to acquire 3468 ETH, with an average cost of $3208.8 per ETH, resulting in an unrealized loss of $420,000.
You may also like

Particle Founder: The entrepreneurial insights I have gained the most from in the past year

Huang Renxun's latest podcast transcript: The future of Nvidia, the development of embodied intelligence and agents, the explosion of inference demand, and the public relations crisis of artificial intelligence

OKX Ventures Research Report: AI Agent Economic Infrastructure Research Report (Part 1)

The migration of settlement rights: B18 and the institutional starting point of on-chain banks

From Tencent and Circle: Looking at the Simple and Difficult Questions of Investment

The second half of stablecoins no longer belongs to the crypto circle

Cursor "Shell" Kimi Controversy Reversed: From Copyright Infringement Allegations to Authorized Collaboration, China's Open Source Model Once Again Becomes a Global AI Foundation

The Real Reason Tokens Don't Sell: 90% of Crypto Projects Overlook Investor Relations

Is the income of pump.fun real, earning a million dollars a day despite the market downturn?

The real reason why tokens are not selling: 90% of crypto projects neglect investor relations

Who is the true winner of the "Tokenization" narrative?

Moss: The Era of AI-Traded by Anyone | Project Introduction

Chip Smuggling Case Exposes Regulatory Loophole | Rewire News Evening Update

How a Structured AI Crypto Trading Bot Won at the WEEX Hackathon
Ritmex demonstrates how disciplined risk control and structured signals can make an AI crypto trading bot more stable and reliable on WEEX, highlighting the importance of combining execution discipline with scalable AI trading systems.

Old Indicator Fails, Three Major New Signals Emerge: BTC True Bottom May Still Be Below $60K

Meeting OpenClaw Founder at a Hackathon: What Else Can Lobsters Do?

Huang Renxun's Latest Podcast Transcript: NVIDIA's Future, Embodied Intelligence and Agent Development, Soaring Demand for Inferencing, and AI's PR Crisis
How a Structured AI Crypto Trading Bot Won at the WEEX Hackathon
Crypto_Trade shows how structured inputs and controlled adaptability can build a more stable and reliable AI crypto trading bot within the WEEX AI Trading Hackathon, highlighting a practical path toward scalable AI trading systems.