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Why I joined the GetAgent AI Trading Bot event: I joined the Bitget GetAgent AI Trading Bot event because I wanted to understand how AI copy trading actually works in real market conditions. Not just which bot shows the highest number, but how each AI thinks, manages risk, and behaves when the market is uncertain. Once I started looking closely, I realized that choosing an AI agent is not a simple decision. Each agent follows a completely different logic, and those differences show clearly in their performance, drawdowns, and trade behavior. Comparing the AI agents by strategy and performance: The first agent that stood out to me was Infinite_Grid. At the time I observed it, it was showing a profit rate around 9%, which was the strongest among all agents. Its strategy is contrarian cycle trading. It assumes price moves in cycles and focuses on buying weakness and selling strength instead of chasing trends. It held mostly long positions on major coins like BTC, ETH, BNB, SOL, XRP, and LTC, using moderate leverage between 5x and 8x. Even though it experienced volatility, it showed the ability to recover from drawdowns. Pure_DeepSeek was the second strongest performer, with a profit rate around 5.5%. Its strategy is adaptive and flexible. It does not follow strict rules and can switch between scalping and swing trading depending on market conditions. At the time, it held long positions on BTC and SOL and kept many assets on wait. This agent felt cautious and focused on capital preservation when signals were unclear. Apex_Neutral had a profit rate around -9.5%. Its approach is market neutral. It opens both long and short positions at the same time to reduce directional risk. It traded assets like BTC, ETH, SOL, and XRP using higher leverage around 12x, but only entered when confidence was high. Even though performance was negative during this period, its risk control and patience were very clear. Dip_Sniper showed a profit rate around -25%. Its strategy focuses on detecting trend exhaustion and early reversals using divergence signals like RSI and MACD. At the time I observed it, it had no open positions and was mostly waiting for clear setups. This showed discipline, but also highlighted how difficult reversal trading can be when timing is not perfect. BlueChip_Alpha was sitting around -55%. It uses a cross-sectional ranking strategy on large-cap coins such as BTC, ETH, BNB, SOL, DOGE, UNI, and XRP. It goes long on strong assets and short on weaker ones, usually with leverage around 10x. This approach is complex and clearly more sensitive to market conditions. Altcoin_Turbo had a profit rate close to -65%. It focuses on altcoins like ADA, UNI, SOL, and BNB, pairing long and short positions to isolate momentum. Even with hedging, the volatility in altcoins made this strategy very challenging during the observed period. CTA_Force was also near -65%. It follows a directional trend strategy using momentum and volume filters. At the time, it was only holding a long BNB position with 10x leverage and waiting on other assets. This showed how trend-following systems can struggle when markets are not trending clearly. What the numbers taught me about market conditions: Looking at all agents together made one thing very clear. This market phase was not friendly to pure momentum or aggressive trend-following strategies. Agents focused on altcoins, high leverage, or strict trend continuation were under pressure. The agents that handled conditions better were the ones that were either adaptive or contrarian. Infinite_Grid and Pure_DeepSeek stood out not because they avoided losses entirely, but because their logic matched the market environment better. How I think about switching between AI agents: From this comparison, I formed a simple rotation logic. When the market is choppy, range-bound, or showing signs of exhaustion, Infinite_Grid makes sense as a base agent. Its cycle-based logic and moderate leverage help control risk. When volatility increases and trends become less predictable, switching part of exposure to Pure_DeepSeek makes sense. Its adaptive behavior allows it to slow down or change style when signals are mixed. During very uncertain or unstable periods, Apex_Neutral can be useful to reduce directional exposure, even if returns are slower. Agents like Dip_Sniper, BlueChip_Alpha, Altcoin_Turbo, and CTA_Force require very specific market conditions. They may perform well in strong trends or clean reversals, but during this period, the data showed that patience was needed before allocating to them. This helped me understand that rotating between AI agents based on market behavior is more important than sticking to one bot permanently. Why I chose Infinite_Grid as my main agent: After comparing strategies and performance, I chose Infinite_Grid as my main copy trading agent. Its profit rate around 9%, combined with its calm behavior and moderate leverage, aligned well with my risk tolerance. I also liked that it showed a clear recovery after a drawdown instead of overtrading. Another important factor for me was that it uses 0% profit sharing, which made testing and observing the strategy more transparent. What actually happened in my own trades: In my own account, one BNB trade closed with a small realized profit. It was a long position using 8x leverage that opened and closed on the same day. The gain was small, but the execution was clean and disciplined. I also have open positions on ETH and SOL that were currently showing small unrealized losses. These positions use 5x leverage and have no liquidation risk. This fits the cycle-based logic of Infinite_Grid, which expects price to move back and forth before resolving. Seeing both realized gains and unrealized losses helped me understand that this strategy is about patience and risk control, not instant results. What this experience taught me about AI copy trading: This event taught me that AI copy trading is not about finding the perfect bot. It is about understanding how each AI thinks, how it performs in different conditions, and how to rotate between strategies when the market changes. The Bitget GetAgent platform made it easy to compare agents side by side, observe real behavior, and learn from both profits and drawdowns. That learning process was the most valuable part of this experience. For me, Infinite_Grid fit best in this market phase, but seeing all agents together helped me build a clearer and more disciplined approach to AI trading going forward.

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UNI Price Prediction: $8.50 Target by February 2025 as Technical Recovery Signals Emerge UNI price prediction points to $8.50-$10.66 recovery potential over 4-6 weeks as oversold conditions and whale accumulation suggest bullish reversal from current $5.43 levels.

$UNI Analysis : The price of UNI is preparing to start an upward movement after gathering liquidity in the range of $4.73 - $5.10. If the price reacts to this zone, we will see growth to the important level of $7.