Bitcoin Magazine

Bitcoin Closes at $114,530 Amid FOMC Volatility: Bulls Eye $117,600 Resistance

Bitcoin Price Weekly Outlook

Bitcoin’s price action was rather subdued last week, keeping traders guessing whether or not we would see another large drop in price entering the weekend. Price held above the lows, however, slowly plodding a little bit higher to close out the week at $114,530. Bulls should not be overly disappointed with this price action, as they did reclaim the $112,200 resistance level, and are now closing in on conquering the next resistance level at $115,500. The bears are still sitting comfortably in control, though, with stronger resistance levels hanging overhead that the bulls have yet to challenge. This may be an interesting and volatile week ahead, with the FOMC meeting on Wednesday and a slough of large companies reporting third-quarter earnings.

Key Support and Resistance Levels Now

Nothing has materially changed from last week’s resistance levels as the bulls have made little progress. Heavy resistance is still sitting at $117,600 and $122,000 above there, so the bears aren’t feeling any real pressure yet. If by chance this week gets above $122,000, we will look to the upper boundary of our broadening wedge pattern at $128,000.

Holding above the prior week’s low is a positive sign for the bulls, while they managed to maintain price above the key short-term support of $106,900 last week as well. This level must hold going forward, as closing below $106,900 opens the door back down to the $105,000 to $102,000 support zone that has already been tested twice. A third test of this support zone would be more likely to break it than to hold it. $96,000 is the long-term bull market support below here, a do-or-die support level if the price were to slide down and test it.

Outlook For This Week

Expect significant volatility this week, especially on Wednesday, as we have the Federal Reserve’s interest rate decision and ensuing Powell speech, followed by major earnings reports from Microsoft, Meta, and Google after market close. Bulls will look to hold $109,000 as a floor into this week, as doing so would position them to maintain upward momentum. Looking at the Momentum Reversal Indicator, we are currently sitting on an 8-count entering Monday. This is a warning candle that we may see momentum begin to fade. Tuesday should bring the 9-count at which point we should expect at least a pause on upward momentum and a 1 to 4 day correction in price. So if bulls can push price up to the 0.618 Fibonacci Retracement at $117,600 by Monday night or Tuesday morning, we should expect to see a rejection ther,e and we can re-assess after Wednesday’s FOMC and earnings reports play out.

Market mood: Bearish – While the bulls gained some ground last week, the bears remain stoic and strong. The bulls must push the price past $122,000 to take back control.

The next few weeks
If bulls can manage to survive through this week, there are still some potential headwinds on the horizon. The US-China tariff dispute may or may not be resolved by the end of next week; a negative outcome will likely send all markets lower. Additionally, the US courts’ ruling on the legality of Trump’s tariffs is expected by November 5th. If these tariffs are reinstated, we should expect markets to head lower to price this impact in.

Terminology Guide:

Bulls/Bullish: Buyers or investors expecting the price to go higher.

Bears/Bearish: Sellers or investors expecting the price to go lower.

Support or support level: A level at which the price should hold for the asset, at least initially. The more touches on support, the weaker it gets and the more likely it is to fail to hold the price.

Resistance or resistance level: Opposite of support.  The level that is likely to reject the price, at least initially. The more touches at resistance, the weaker it gets and the more likely it is to fail to hold back the price.

Fibonacci Retracements and Extensions: Ratios based on what is known as the golden ratio, a universal ratio pertaining to growth and decay cycles in nature. The golden ratio is based on the constants Phi (1.618) and phi (0.618).

Broadening Wedge: A chart pattern consisting of an upper trend line acting as resistance and a lower trend line acting as support. These trend lines must diverge away from each other in order to validate the pattern. This pattern is a result of expanding price volatility, typically resulting in higher highs and lower lows.

Momentum Reversal Indicator (MRI): A proprietary indicator created by Tone Vays. The MRI indicator tracks buyer and seller momentum and exhaustion, providing signals to indicate when to expect momentum to fade and accelerate.

This post Bitcoin Closes at $114,530 Amid FOMC Volatility: Bulls Eye $117,600 Resistance first appeared on Bitcoin Magazine and is written by Ethan Greene - Feral Analysis and Juan Galt.

Bitcoin Magazine

Alpha Arena Reveals AI Trading Flaws: Western Models Lose 80% Capital in One Week

Can AI trade crypto? Jay Azhang, a computer engineer and finance bro from New York, is putting this question to the test with Alpha Arena. The project pits the greatest large language models (LLM) against each other, each with 10 thousand dollars worth of capital, to see which can make more money trading crypto. The models include Grok 4, Claude Sonnet 4.5, Gemini 2.5 pro, ChatGPT 5, Deepseek v3.1, and Qwen3 Max. 

Now, you might be thinking “wow, that’s a great idea!” and you would be surprised, at the time of writing, three out of the five AIs are underwater, with Qwen3 and Deepseek — the two Chinese open source models — leading the charge. 

That’s right, the western world’s most powerful, closed source, proprietary artificial intelligences run by giants like Google and OpenAI, have lost over $8,000 dollars, 80% of their crypto trading capital in little over a week, while their eastern open source counterparts are in the green.

The most successful trade so far? Qwen3 — moisturised and in its lane — with a simple 20x bitcoin long position. Grok 4 — to no one’s surprise — has been long Doge with 10x leverage for most of the competition… having at one point been at the top of the charts along with Deepseek, now close to 20% underwater.  Maybe Elon Musk should tweet a doge meme or something to, you know, get Grok out of the dog house. 

Meanwhile, Google’s Gemini is relentlessly bearish, being short on all the crypto assets available to trade, a position that echoes their general crypto policy over the past 15 years. 

Last but not least is ChatGibitty, which has made every bad trade possible for a week straight, a remarkable achievement! It takes skill to be that bad, especially when Qwen3 just longed bitcoin and went fishing. If this is the best closed-source AI has to offer, then maybe OpenAI should just keep it closed source and spare us.

A new benchmark for AI

All joking aside, the idea of pitting off AI models against each other in a crypto trading arena has some very profound insights. For starters, AI can not be pre-trained on answers to knowledge tests with crypto trading since it is so unpredictable, an issue that other benchmarks suffer from. To put it another way, many AI models are being given the answers to some of these tests in their training, and so of course they perform well when tested. But some research has demonstrated that slight changes to some of these tests lead to radically different AI benchmark results.

This controversy begs the question: What is the ultimate test of intelligence? Well, according to Elon Musk, Iron Man enthusiast and creator of Grok 4, predicting the future is the ultimate measure of intelligence. 

The ability to predict the future is the best measure of intelligence https://t.co/W6WriRGt9N

— Elon Musk (@elonmusk) September 5, 2025

And let’s face it, there’s no future more uncertain than the short-term price of crypto. In the words of Azhang, “Our goal with Alpha Arena is to make benchmarks more like the real world, and markets are perfect for this. They’re dynamic, adversarial, open-ended, and endlessly unpredictable. They challenge AI in ways that static benchmarks cannot. — Markets are the ultimate test of intelligence.” 

This insight about markets is deeply embedded in the libertarian principles from which Bitcoin was born. Economists like Murray Rothbard and Milton Friedman made the case over a hundred years ago that markets were fundamentally unpredictable by central planners, that only individuals making real economic decisions with something to lose could make rational economic calculations.

In other words, the market is the most difficult thing to predict as it depends on the individual perspectives and decisions of intelligent individuals throughout the world, and thus, it is the best test of intelligence.

Azhang mentions in its project description that the AIs are instructed to trade not just for gains, but for risk-adjusted returns. This risk dimension is critical, as one bad trade can wipe out all previous returns, as seen, for example, in the downfall of Grok 4’s portfolio. 

There’s another question that remains, which is whether these models are learning from their experience trading crypto, a matter that is not technically easy to achieve, given that AI models are very expensive to pre-train in the first place. They could be fine-tuned with their own trading history or other people’s history, and they might even keep recent trades in their short-term memory or context window, but that can only take them so far. Ultimately, the right AI trading model might have to really learn from its own experiences, a technology that was recently announced among academic circles but has a long way to go before it becomes a product. MIT calls them self-adaptating AI models. 

How do we know it is not just luck? 

Another analysis of the project and its results so far is that it may be indistinguishable from a ‘random walk’. A random walk is akin to throwing dice for every decision. What would that look like on a chart? Well, there’s actually a simulator you can use to answer that question; it would not look too different, actually. 

This question of luck in markets has also been described quite carefully by intellectuals like Nassim Taleb in his book Antifragile. In it, he argues that from the perspective of statistics, it is perfectly normal and possible for one trader, say Qwen3 in this case, to be lucky for a whole week straight! Leading to the appearance of superior reasoning. Taleb goes a lot further than that, arguing that there are enough traders on Wall Street that one of them could easily be lucky for 20 years in a row, developing a god-like reputation, with everyone around them assuming this trader is just a genius, until, of course, luck runs out. 

Thus, for Alpha Arena to produce valuable data, it will actually have to run for a long time, and its patterns and results will need to be replicated independently as well, with real capital at stake, before they can be identified as different than a random walk.

Ultimately, it’s great to see the open-source, cost-efficient models like DeepSeek outperform their closed-source counterparts so far. Alpha Arena has so far been a great source of entertainment, as it has gone viral on X.com over the past week. Where it goes is anyone’s guess; we will have to see if the gamble its creator took, giving $50,000 to five chatbots to gamble on crypto with, pays off in the end. 

This post Alpha Arena Reveals AI Trading Flaws: Western Models Lose 80% Capital in One Week first appeared on Bitcoin Magazine and is written by Juan Galt.