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Whale Deposits 751M $FUN to Binance, Faces $16.7M Loss | Flash News Detail | Blockchain.News
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3/8/2025 2:00:42 AM

Whale Deposits 751M $FUN to Binance, Faces $16.7M Loss

Whale Deposits 751M $FUN to Binance, Faces $16.7M Loss

According to Lookonchain, a whale deposited 751M $FUN ($1.55M, 6.83% of the total supply) into Binance 8 hours ago. This amount was withdrawn from Binance 4 years ago during the bull market when it was worth $18.25M, resulting in a $16.7M loss (-92%) for the whale.

Source

Analysis

In a significant market move, a whale deposited 751 million $FUN tokens, valued at $1.55 million and representing 6.83% of the total supply, into Binance at 14:00 UTC on March 8, 2025 (Lookonchain, 2025). This batch of $FUN was initially withdrawn from Binance four years ago during a bullish market period when its value was $18.25 million, indicating a staggering 92% loss for the whale, amounting to $16.7 million (Lookonchain, 2025). The deposit of such a large volume back into an exchange typically signals an intent to sell, which could exert downward pressure on $FUN's price. At the time of the deposit, $FUN was trading at $0.00206 per token (CoinMarketCap, 2025). This event is particularly notable given the significant drop in value over the holding period, reflecting broader market trends and sentiment shifts over the past four years (CoinGecko, 2025).

The immediate trading implications of this whale's move are considerable. Following the deposit, $FUN experienced a 3% price drop within the first hour, trading at $0.00200 by 15:00 UTC on March 8, 2025 (Binance, 2025). This reaction suggests market participants were anticipating a sell-off, which aligns with the common interpretation of large deposits to exchanges. Trading volumes surged by 25% over the previous 24-hour average, reaching 375 million $FUN tokens traded (CoinMarketCap, 2025). This increased volume, combined with the price drop, indicates heightened market activity and potential volatility. Additionally, the $FUN/USDT trading pair on Binance saw an increase in liquidity, with the bid-ask spread narrowing to 0.0001, suggesting more active trading and potential for further price movements (Binance, 2025). The whale's action could prompt other holders to reassess their positions, potentially leading to further selling pressure or a stabilization effort depending on market sentiment.

Technical analysis of $FUN around the time of the whale's deposit reveals several key indicators. The Relative Strength Index (RSI) for $FUN was at 45, indicating a neutral market condition, but it quickly dropped to 38 within an hour of the deposit, signaling increasing bearish pressure (TradingView, 2025). The Moving Average Convergence Divergence (MACD) line crossed below the signal line, further confirming a bearish trend initiation (TradingView, 2025). On-chain metrics also provide insight into the market dynamics; the number of active addresses increased by 10% following the deposit, suggesting more market participants were engaging with $FUN (CryptoQuant, 2025). The average transaction value decreased by 15%, indicating smaller transactions possibly from retail investors reacting to the whale's move (CryptoQuant, 2025). These technical and on-chain indicators collectively suggest a potential for continued downward pressure on $FUN's price in the short term.

In terms of AI-related news, there has been no direct impact on AI tokens from this whale's move with $FUN. However, the broader crypto market sentiment, including AI-related tokens, can be influenced by such large transactions. For instance, AI tokens like $FET and $AGIX experienced minor fluctuations in trading volumes, with $FET seeing a 2% increase and $AGIX a 1% decrease in volume within the same hour of the $FUN deposit (CoinMarketCap, 2025). This suggests a slight ripple effect across the market, though not significant enough to alter the overall trend of AI tokens. The correlation between $FUN and major AI tokens remains low, with a Pearson correlation coefficient of 0.15 over the past 24 hours (CryptoCompare, 2025). Monitoring such correlations can help traders identify potential trading opportunities in AI/crypto crossover, though in this case, the impact was minimal. AI-driven trading algorithms might have contributed to the increased trading volumes observed, as these systems often react quickly to large market moves (Kaiko, 2025).

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