Get a free 3Commas plan this Christmas 🎄 We’re giving away Expert, Pro, and Starter plans. To enter: 1. Write a short Christmas story on X. 2. Post it as a reply or on your own profile. 3. Tag @3commas and use #3CommasChristmas. Full rules: bit.ly/3MDbtXs 📄 This campaign is not available to users located in the EEA

Who wins? 👀 @Cobratate makes his MFB debut tomorrow night vs the heavyweight champ @ChaseDeMoor 🏆 Watch live on Rumble Premium 🍿

Will 2026 be BULLISH🐂 or BEARISH🐻 for Bitcoin $BTC? That's what we were wondering in our 1000 person investors group. Which is why we decided to do a Mission to track all the top crypto/macro analysts to see what they're predicting for 2026. 1 week later, here are our results: • 86 predictions tracked • 61% were bullish • 39% were bearish • Highest price we saw was $180-200k • Lowest price we saw was $40-50k • Most bullish were institutions and macro analysts • Most bearish were chart and 4-year cycle analysts • Notable analysts tracked: Ben Cowen, Lyn Alden, Tom Lee, Mike Alfred, Henrik Zeberg, Bob Loukas, Joao Wedson, Ki Young Ju, BitcoinData21, and more!

One last note on the Fear & Greed Index When people encounter the Fear and Greed Index in their feeds, they typically operate under a straightforward assumption: • low index values predict positive future returns • high index values predict negative future returns In other words, the index functions as a mean reversion indicator: when markets are overbought (extreme greed) or oversold (extreme fear), people expect that prices will revert toward the mean from these extremes. This is a hypothesis we can test. We can examine actual returns following extreme index readings. An easy and fast way to do this is to plot returns against index values and visually inspect the relationship. Under our mean reversion hypothesis, we would expect to see: • low index values: data points clustering top left • high index values: data points clustering bottom right The results reveal a lot of noise, but also a pattern, particularly in extreme greed territory: the indicator frequently fails during trending markets. When you trade based on overbought or oversold signals, you're essentially positioning against the prevailing trend. In practical terms, this means buying too early in downtrends and selling too early in uptrends. Instead of confirming our hypothesis, we not only find considerable noise but sometimes even the opposite of what we would expect to happen. Obviously, you can discuss how this approach could be slightly tweaked to improve performance. Criticism is welcome, as always. My point is: this is a metric influencers love to hype, but as the chart shows, it’s mostly just noise :)