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Wintermute: Bitcoin has fallen for the third consecutive week, with $80,000 becoming a key support level

Market maker Wintermute stated that Bitcoin fell 2.43% last week, marking its third consecutive week of losses, dropping below the support level of $82,500 and testing the $80,000 mark again. Macroeconomically, rising energy prices and inflation concerns are replacing AI financing issues as the main risks in the market. Bitcoin's recent movements are highly correlated with the Nasdaq and AI-related stocks, and ETF fund flows have begun to fluctuate in both directions.Wintermute indicated that the Federal Reserve's interest rate decision and policy statements will be important short-term catalysts. If it fails to hold above $80,000, the next key support level is $76,000; if it effectively breaks below this, the price may further explore the $70,000 range. In the short term, it needs to regain the $82,000 to $82,500 range to have a chance of restoring an upward trend. If the Federal Reserve leans hawkish, the market may reassess the expectations for a rate hike in December, raising investment yields and increasing downward pressure on Bitcoin; if it leaves room for a December decision, it may create conditions for a rebound.The institution stated that before the Federal Reserve's decision and the release of the U.S. third-quarter GDP and September PCE, market directional confidence is insufficient, and caution will be maintained. If the Federal Reserve leans dovish but the PCE is higher than expected, the market may sell off tech stocks due to inflation pressure, dragging down Bitcoin. Wintermute emphasized that it is not clearly bearish at the moment, but remains cautious until Bitcoin regains the $82,500 level.

Analyst: In each U.S. midterm election cycle, Bitcoin has shown a significant pullback, and attention should be paid to the key support level of $73,000

Cryptocurrency analyst Ali Charts stated that as the U.S. midterm elections on November 3 approach, investors may overlook the recurring phenomenon of Bitcoin pullbacks observed in previous election cycles. He believes that this election may once again be accompanied by significant volatility, and the downside risk is worth noting, with a key focus on the lower price level of $73,000. According to his statistics, during the U.S. midterm election cycles in 2010, 2014, 2018, and 2022, Bitcoin experienced pullbacks of 72%, 65%, 52%, and 27%, respectively.However, he emphasized that correlation does not imply causation; the election itself may not necessarily be the reason for the decline, as these pullbacks are also influenced by the overall market cycle of Bitcoin, liquidity conditions, and investor sentiment. Fourth-quarter returns also exhibit similar characteristics: in 2014, 2018, and 2022, Bitcoin fell by 16.7%, 42.16%, and 14.75% in the fourth quarter, respectively. The only exception was in 2010, when it rose by 391%, but Bitcoin was still in its early development stage at that time. Therefore, in the past four midterm election years, three of those years recorded negative returns in the fourth quarter.Regarding the subsequent trend, Ali Charts indicated that the area around $73,000 is close to the average purchase cost of current short-term Bitcoin holders, which can be used to observe the positions of recent investors entering the market. He pointed out that in an established bull market, Bitcoin often finds support near the cost of short-term holders after experiencing a significant pullback. He believes that if selling pressure accelerates after the election on November 3, pushing Bitcoin back down to around $73,000, that area will become an important position to test whether buyers are still willing to maintain the overall bullish trend. If the price continues to rebound from that area, it will strengthen the bullish judgment; in terms of operations, he will wait for signs of selling pressure exhaustion and renewed demand before considering establishing long positions.

Bitget deepens its RWA strategic layout, building an institutional-level liquidity framework with Super Match

According to official news from Bitget, the platform announced an upgrade to its RWA strategy, further expanding its tokenized stock business from asset trading access to institutional-level liquidity infrastructure construction. This upgrade introduces the Super Match Engine, breaking through the traditional single model of direct connection to brokers, adopting an internal matching priority mechanism, and providing market makers with lower latency trade execution, clearer liquidity distribution, and more efficient capital utilization through exclusive quoting channels and layered depth market data. At the same time, Super Match supports differentiated order processing and market services to meet the diverse trading and operational needs of institutional traders.Previously, Bitget launched the rToken US stock transfer function on October 13, supporting over 3,000 US stocks and ETFs. Users can transfer eligible stock assets from traditional brokerage accounts to Bitget, and once the stocks are credited, they will be automatically converted into US stock spot rTokens based on the corresponding number of shares. On this basis, relying on the Super Match framework, the efficiency of US stock trading will be continuously enhanced.Bitget CEO Gracy Chen stated that the next phase of tokenized asset development is crucial for bridging the asset and liquidity channels between traditional finance and the digital asset market. Bitget will continue to improve cross-market trading infrastructure, promote the application of rToken in scenarios such as spot trading, lending, margin, and asset management, enhance asset liquidity and capital efficiency, and deepen the strategic layout of the panoramic exchange (UEX).

The new model from Zhipu has been reported to have up to 3.5 trillion parameters, expected to reach Claude Fable level

According to Beating AI's news, a summary of an expert interview circulating in the investment community states that the next-generation foundational model of the artificial intelligence company Zhipu is not the previously rumored 1.6 trillion parameters, but rather between 2.5 trillion and 3.5 trillion parameters. The GLM-5.3 released by Zhipu in August this year still uses the previous generation foundational model, with a total of approximately 744 billion parameters; if the information is true, the new foundational model's scale is about 3.4 to 4.7 times that of the current one.The summary also states that the training has not gone smoothly, as Zhipu adopted a more aggressive model architecture, and during the pre-training period, there was a time when the loss did not decrease, meaning that computational power was continuously invested but training metrics could not continue to improve. The interviewee believes that the problem should have been resolved recently, and training is expected to be completed soon, predicting that the new model can reach flagship levels comparable to Claude Fable.The summary further mentions that the new model has taken inference speed and cost as hard constraints from the architectural design stage, with the goal of being able to provide services on a large scale from the first day of release. At the same time, multiple cloud vendors are negotiating revenue sharing to address the issue of large models being produced but not being able to supply.
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