Top Tweets for #GETAGENT
AI Smart Grid: My Experience So Far
I went into Smart Grid expecting another “set it and forget it” bot.
What I found was more interesting.
Discover → Build → Customize → Adapt → Share
Right after starting, #GetAgent identified $SOL and $ETH for spot grids, but with different ranges and allocations. That immediately caught my attention, it wasn't simply applying one configuration to every asset.
The AI also explains that it can rescan the market every 4 hours, score symbols based on factors such as trend strength and volatility, and switch toward more suitable trading pairs.
I could actually see this decision-making process in action.
In one review, 24 symbols went through comprehensive decision making, with 2 selected as new candidates, while others were placed on watch or rejected based on their scores and ranking.
And the experience wasn't just about selection.
I started with a Stable configuration, then monitored the strategy through its NAV curve and decision log. My screenshot showed the strategy at +0.61% total ROI, with the curve reaching +0.84% at one point.
That gave me a different perspective on automated trading.
A traditional grid can automate order execution, but @bitget AI Smart Grid adds another layer, monitoring, evaluating and adapting the strategy as market conditions change.
My biggest lesson is that AI doesn't eliminate risk. It also shouldn't be treated as a guaranteed-profit machine.
What it can do is take some of the repetitive decision-making off the trader's hands while continuously reassessing the market.
Would I use it again? Yes.
The feature I find most valuable is the adaptation, because markets don't stay still, so having a strategy that can reassess instead of relying entirely on my original settings is meaningful.
For me, Smart Grid changed the question from:
“Can a bot trade for me?”
to:
“How much smarter can the automation become?”
That’s the part of the journey I’ll keep watching.
I wanted to see what actually changes when AI is added to grid trading, so I looked deeper into #GetAgent’s Smart Grid on @bitget
The difference from a normal grid is bigger than I expected.
With a traditional grid, I would normally have to:
• Pick the trading pair
• Analyze the market myself
• Set the upper/lower range
• Choose grid count
• Decide leverage
• Monitor the strategy and adjust it when conditions change
With AI Smart Grid, the process becomes much more dynamic.
In my test, GetAgent fetched market data and indicators across 24 symbols, then performed a comprehensive decision process.
What really caught my attention:
24 symbols were evaluated.
2 were selected as new candidates.
9 were rejected.
And it didn't simply treat every coin the same.
Some were marked “Watch” because their score was below the creation threshold, while others exceeded the candidate ranking limit.
It also performed risk control checks using things like the bot range, take-profit/stop-loss settings, capital allocation and return data.
That’s a completely different workflow from manually choosing a coin and hoping the grid parameters remain suitable.
The AI capability I found most interesting is therefore candidate selection + continuous evaluation.
A normal grid essentially follows the rules I give it.
Smart Grid can evaluate multiple opportunities, filter candidates and make decisions based on the information it processes.
And according to the GetAgent setup, it can also rescan the market every 4 hours and switch toward more suitable trading pairs.
So my takeaway:
Normal Grid = I do most of the decision-making.
AI Smart Grid = AI assists with selection, configuration, screening and adaptation.
It doesn't eliminate trading risk, but it can remove a lot of the repetitive manual work.
That’s the part of AI Smart Grid that stood out to me most.
One thing I’ve learned from building trading strategies with AI:
The hardest part isn’t finding a market. It’s knowing when to move between markets.
For the final Trade-thon challenge, I built Adaptive Cross-Asset Rotation with @bitget #GetAgent Studio.
The idea behind it was simple: instead of building a strategy that depends entirely on one asset class, I wanted to explore whether AI could help structure a system that looks across different markets and adapts to where the better opportunity is.
Why cross-asset?
Markets don’t always move together.
There are periods when crypto leads, periods when equities offer stronger momentum, and periods when precious metals become more interesting.
So rather than forcing the same setup every time, my Playbook is designed around relative opportunity, trend conditions and momentum across multiple markets.
How the strategy works
The Playbook evaluates the available markets for stronger directional conditions.
When the required conditions align, it looks to participate in the stronger opportunity.
When conditions become weaker or unclear, the strategy becomes more selective instead of simply trading because a signal appeared.
The goal isn't to predict every move.
It's to create a structured process for:
Identify → Compare → Rotate → Manage risk → Review
Risk management
I also wanted the strategy to understand that being active isn't the same as being profitable.
The Playbook therefore uses controlled exposure and risk management rather than treating every signal as an opportunity that must be taken.
That matters especially in cross-asset trading because weakness in one market can quickly change the overall picture.
The result so far
My current backtest isn't something I'm pretending is perfect:
Backtest return: -1.3%
Maximum drawdown: -3.3%
But this is exactly why I like testing these ideas.
A negative result gives me something specific to investigate instead of convincing myself that an idea works simply because it sounds good.
What I’ve learned
The biggest surprise from building these Playbooks has been how different a trading idea becomes once you have to turn it into actual rules.
AI makes it easier to structure and test the idea.
But the human still has to ask:
Does the logic actually make sense?
Where does it fail?
What happens when market conditions change?
That’s where my focus is now.
I’m refining the rotation logic, improving market selection and trying to make the Playbook more selective during weak or choppy conditions.
This Trade-thon has changed how I look at AI trading.
For me, AI isn't about handing over the decision making.
It’s about turning an idea into something I can actually test, challenge and improve.
Still refining this Playbook.
Would you rather build one strategy across multiple markets, or keep a separate strategy for each asset class?
RUNECLAW ETHUSDT Daily Breakout — Paper Return (live) +13.0% on GetAgent Studio https://t.co/XvEhc8dB6L #Bitget #GetAgentStudio #GetAgent #Playbook
I’m building a Precious Metals Playbook with @bitget #GetAgent:
$XAUUSDT & $XAGUSDT Metals EMA Pullback.
I chose gold and silver because they can deliver strong continuation moves when trend, momentum and market conditions align.
Here’s the logic behind it:
• Trend: Higher timeframe fast EMA must be above the slow EMA for longs, with directional strength confirming momentum.
• Entry: Wait for a pullback into the trend, then enter when momentum confirms continuation.
• Exit: ATR based protective stop, partial profit taking, breakeven protection and trailing logic help manage the position as the move develops.
• Risk: Controlled position sizing with volatility based stops instead of forcing a fixed distance.
The Playbook is designed mainly for clean trending conditions and should avoid getting chopped up in sideways markets or during sudden macroeconomic shocks.
Current backtest: -0.47% return | -0.5% max drawdown.
I’m not calling it finished. That result showed me exactly why refinement matters.
I’m now working on better entries and stronger filters without overfitting the strategy.
That’s the part I enjoy about building with AI: take a trading idea, turn it into executable logic, test it, find the weakness, and improve it.
What would you improve first: the entry confirmation or the market condition filter?
#BitgetAI
Building an AI That Trades US Stocks My Way
One thing I've learned from trading is that good strategies aren't built in one attempt, they're refined through testing, data, and continuous improvement.
That's exactly what I've been doing with my Multi Timeframe Stock Trend Playbook on @bitget #GetAgent.
Instead of chasing every market move, I wanted to build a strategy that focuses on high quality trends in some of the world's strongest companies: $NVDA, AAPL, MSFT, META, and $TSLA.
Here's how my Playbook works:
• Trades long only to align with the broader market trend.
• Uses the 4H timeframe for execution while the Daily trend acts as a confirmation filter.
• Risks only 0.7% per trade, with ATR-based position sizing so exposure automatically adjusts to market volatility.
• Takes partial profits, moves the stop-loss to breakeven, trails winning positions, and exits early if the trend weakens.
• Closes trades after a maximum of 30 candles to avoid overstaying positions.
I chose US stocks because they consistently produce strong momentum and trend opportunities, especially in leading technology companies.
My goal isn't to predict every move, it's to participate only when multiple conditions align.
The latest Cloud backtest gave me a solid foundation:
• +1.1% return
• 115 executed trades
• 7.07% maximum drawdown
• 38% win rate
• 1.08 profit factor
These aren't "perfect" results, and that's okay. To me, a realistic strategy with room for improvement is far more valuable than an over-optimized backtest that won't survive live markets.
As the GetAgent Playbook Trade-thon continues, I'll keep refining the entry filters, improving risk management, and reducing unnecessary trades to make the strategy more robust across different market conditions.
If the data continues to improve and the strategy remains consistent, I'd be confident taking it from paper trading to live execution.
This is what I enjoy most about Bitget GetAgent: it transforms a trading idea into an executable AI Playbook, then gives you the tools to test, measure, refine, and improve it based on evidence instead of emotion.
I'm looking forward to seeing how far this Playbook can evolve throughout the competition.
What would you improve first in a trend following AI strategy: entries, exits, or risk management?
$NVDA momentum breakout is cooking
paper return already sitting at +4.4% on GetAgent Studio
volume said “send it” and NVDA listened
just paper trading for now… but the setup looking kinda degen
#Bitget #GetAgentStudio #GetAgent #Playbook
XAU Combined Trend Breakout Paper Return (live) +9.3% on GetAgent Studio
https://t.co/7v90a0yhBR
#Bitget #GetAgentStudio #GetAgent #Playbook
XAU Combined Trend Breakout Paper Return (live) +9.3% on GetAgent Studio
https://t.co/7v90a0yhBR
#Bitget #GetAgentStudio #GetAgent #Playbook
fomo is undefeated.
you can spend 6 months learning TA and still get rugged by your own emotions in 6 minutes.
so i built Agent 47.
a trading agent that treats human emotions like a bug, not a feature.
if losses hit 2% - shuts itself down.
if you want to revenge trade - not happening.
if market volatility goes nuclear - cuts risk automatically.
basically the trader i wish i was.
$BTC $BGB
github:
https://t.co/1RuZndcJUD
cc: @Bitget_AI @Bitget_Global
#BitgetHackathon
Just joined the #GetAgent Playbook Trade-thon with my Recovery Opportunity Research Playbook.
Instead of chasing every breakout, I built this strategy to identify high-probability recovery opportunities after sharp market pullbacks.
Here's how it works:
The Playbook continuously monitors BTC, NVDA, and TSLA for signs of recovery using price action, volume, and market structure.
It waits for confirmation before entering, avoiding emotional trades and reducing false signals.
Rather than predicting every move, it focuses on assets showing improving momentum after periods of weakness.
Risk management is built into the strategy, with predefined exits and disciplined position management to protect capital when conditions change.
So far, the results have been encouraging, with a 70% win rate, +4.6% live paper return, and controlled drawdown during testing.
This Trade-thon is a great opportunity to keep refining the strategy with real market conditions and learn from the community.
What type of AI Playbook are you building?
#GetAgent #BitgetTradeThon @Bitget_AI @BitgetBuilders
Excited to be joining the #GetAgent Playbook Trade-thon!
I just published my ATOM Dual Long Trend Setups playbook a strategy built around patience, trend confirmation, and disciplined execution rather than chasing every move.
What I like most about the results so far is that the strategy stays true to the plan.
It doesn't aim to win every trade; it focuses on protecting capital, cutting losing trades early, and letting profitable trades do the heavy lifting over time.
That's the kind of consistency I'm trying to build.
This is only the beginning.
I'll be refining the playbook, tracking every result, and sharing the journey throughout the competition.
Looking forward to seeing how it performs.
Let's build. 📈🔥
Win 1,000 USDT from your trading idea with GetAgent Playbook Trade-thon!
> Create a runnable Playbook and climb the leaderboard to share 20,000 USDT.
> Choose your top pick to share a 5,000 USDT airdrop.
Joining the #GetAgent Playbook Trade-thon with my Cross-Asset Regime Confirmation Strategy. 🌐
Rather than forcing one setup across every market, I built this Playbook around BTC and ETH, focusing on trading only when the market is clearly trending.
The logic is simple:
🔹 Every 4H candle, the Playbook checks the EMA 20/50 to identify the market regime—bullish, bearish, or ranging.
🔹 If a trend is confirmed, RSI validates momentum while ATR confirms there’s enough volatility to justify the trade.
🔹 If the market is ranging or volatility is weak, it simply waits. No forced entries.
Risk management is built in with ATR-based stop-loss and take-profit levels, plus a regime-flip exit. If the trend changes, the position closes automatically instead of relying on emotion.
The goal isn’t to catch every move—it’s to stay with the higher-probability ones.
Looking forward to testing and refining it throughout the Trade-thon.
What kind of Playbook are you building?
#BitgetTradethon #BitgetAI
📉 إذا كنت تمتلك استراتيجية تداول تثق بها، فقد حان الوقت لتحويلها إلى فرصة للفوز. 💰
🏁 أطلقت #GetAgent فعالية Playbook Trade-thon، وهي منافسة مخصصة للمتداولين وصناع الاستراتيجيات، حيث يمكنك إنشاء Playbook قابل للتشغيل، والمنافسة على لوحة الصدارة للفوز بجوائز تصل إلى 20,000 USDT.
🎁 بالإضافة إلى ذلك، يمكنك التصويت لاختيار أفضل Playbook والمشاركة في Airdrop بقيمة 5,000 USDT.
🔹لماذا يستحق الحدث الاهتمام؟
✅ فرصة لإبراز أفكارك واستراتيجياتك أمام المجتمع.
✅ جوائز مجزية لأفضل المشاركات.
✅ إمكانية استكشاف استراتيجيات جديدة والاستفادة من أفكار متداولين آخرين.
📅 إذا كنت تبحث عن تحدٍ جديد يجمع بين الإبداع والتداول، فهذا الحدث يستحق المتابعة.
🔗 تعرف على التفاصيل وشارك:
https://t.co/OjivN6ZDIo
🔹لمتابعة كل التحديثات:
@BitgetMENA
🔹للإنضمام للمجموعة العربية:
https://t.co/dQv1h6L907
#GetAgent #TradeThon #Trading #Crypto #USDT
Everyone talks about using AI to trade. I wanted to see if it could actually follow a strategy without emotions.
That’s why I’m joining the #GetAgent Playbook Trade-thon with my first published Playbook: Large-Cap Breakout.
It targets Bitget RWA US stocks-NVDA, MSFT, AAPL, AMZN, META, and GOOGL…and looks for confirmed breakout opportunities while managing risk with predefined exits and position sizing.
I chose this strategy because large-cap stocks usually produce cleaner momentum than chasing random market moves. Rather than making emotional decisions, I wanted a rules-based approach that can be tested, refined, and improved over time.
This is just the beginning, and I’m looking forward to seeing how the Playbook performs as paper trading continues while learning from other builders in the Trade-thon.
Win 1,000 USDT from your trading idea with GetAgent Playbook Trade-thon!
> Create a runnable Playbook and climb the leaderboard to share 20,000 USDT.
> Choose your top pick to share a 5,000 USDT airdrop.
🤖 قبل أن أجرب #GetAgent كنت أظن أنها مجرد أداة ذكاء اصطناعي أخرى داخل منصة التداول... لكن الفكرة أعمق من ذلك.
🔹GetAgent على Bitget يعمل كمساعد ذكي يساعدك في الوصول إلى المعلومات بشكل أسرع، سواء كنت تبحث عن تحليل للسوق، أو شرح لمنتج داخل المنصة، أو تريد فهم حركة أحد الأصول دون التنقل بين عشرات الصفحات.
أكثر ما أعجبني في الخدمة:
⚡ الوصول السريع للمعلومات من داخل المنصة.
🤖 مساعد يعتمد على الذكاء الاصطناعي لتبسيط تجربة المستخدم.
📊 يساعد في فهم الأسواق ومنتجات Bitget بشكل أسرع.
⏱️ يوفر الوقت ويجعل اتخاذ القرار أكثر كفاءة.
بالطبع، تبقى قرارات الاستثمار مسؤوليتك، لكن وجود مساعد ذكي يوفر لك المعلومات في ثوانٍ يجعل تجربة التداول أكثر سلاسة، خاصة للمستخدمين الجدد.
هل سبق لكم تجربة GetAgent؟ وما أكثر ميزة لفتت انتباهكم ؟
لمتابعة الإعلانات و كل جديد :
@BitgetMENA
للإنضمام للمجموعة العربية:
https://t.co/u2C1O1QXwY
#Bitget #GetAgent #AI #Crypto #Trading #BTC #news
📊 لم تعد بحاجة للتنقل بين عشرات المصادر لفهم السوق.
🤖 مع #GetAgent AI Briefing تحصل على ملخص ذكي يجمع لك كل ما يهم حول أي أصل في مكان واحد:
✅ تحليل فني واضح
📰 أبرز الأخبار المؤثرة
👨💼 آراء وتوقعات المحللين
📈 نتائج الشركات وأهداف الأسعار
💡 مؤشرات معنويات السوق وتفاعل المجتمع
مدعوم بتقنيات #BitgetAI لتمنحك رؤية أسرع وأعمق لأسواق العملات الرقمية والأسهم الأمريكية، حتى تتمكن من اتخاذ قرارات أكثر ثقة في ثوان. 🚀
لمتابعة كل التحديثات:
@BitgetMENA
للإنضمام للمجموعة العربية:
https://t.co/u2C1O1QXwY
I especially agree that AI should reduce the burden of investing, not simply encourage people to trade more.
#GetAgent can already help analyze cross-asset holdings, identify portfolio risks, suggest hedging strategies, and provide allocation ideas. Give it a try 😉 That said, we clearly still have work to do in making these capabilities easier to discover and use.
Thank you for using the product so thoughtfully and for sharing both what is working and where we can improve 🫶
For the past few months, I’ve been trading US stocks almost exclusively on Bitget. What drew me in wasn't just the addition of US stocks and options, but the overarching vision of 'one account for global assets.' I want to share my hands-on experience over this period and a few suggestions for the Bitget team.
My investing used to feel incredibly fragmented. One app for crypto, another for US equities, constantly transferring capital back and forth and dealing with FX conversions. Each platform worked fine in isolation, but the overall trading workflow was full of friction. Bitget feels more like they are steadily consolidating Crypto, US equities, commodities, and eventually more RWAs into one unified account. The real UX upgrade isn't just having more assets to trade; it’s that you no longer have to waste time hopping between platforms and moving liquidity around. You can actually focus your energy on researching the market itself.
Another point that resonates with me is the 'Crypto-native' approach. As crypto users, we’re already hardwired for 24/7 trading, instant settlement, and managing everything from a single wallet. I believe the truly great products of the future won't force crypto natives to adapt to TradFi, but will instead make traditional assets adopt the crypto experience.
That being said, there are still a few features I’d love to see optimized:
First, I'd love to see a Trending / Top Movers / Most Active page. I check these leaderboards every single day at the US market open. Integrating them directly into the UI would make hunting for setups much easier.
Second, it would be great to separate ETFs from individual stocks. Right now, searching for a ticker often pulls up a bunch of ETFs. Adding a simple filter here would significantly improve the search experience.
Third, and this is the direction I’m most excited about: AI.
Bitget already has GetClaw, Agent Hub, and the GetAgent Playbook for creating and running trading strategies.
But what I’m really looking forward to is AI not just executing an individual trade for me, but actively managing my entire portfolio. For instance, analyzing my combined Crypto and US equity holdings to flag if my risk exposure is too high, suggesting whether I need to hedge, or even automatically generating cross-asset allocation advice.
I think the true value of AI isn't in pushing users to trade more, but in reducing the operational burden of trading.
I really appreciate that Bitget is consistently thinking from the user's perspective about what the future of investing should look like.
If Bitget keep executing on this roadmap of Global Assets + Crypto-native + AI reducing the trading burden, Bitget's upside potential might be far bigger than what most people are seeing right now.
I also used #GetAgent to compare different scenarios before finalizing my plan. Bitget makes it easy to trade these events through Stock Futures, Stock 2.0, and Stock+, all with competitive fees, so I can adapt as the market reacts.
#Macro #Trading #Investing
@bitget
One thing I've learned trading US stocks: major events create the biggest opportunities. 🚀
Today, I'm watching $MU earnings and the $NVDA shareholder meeting closely.
I used Bitget Stock+ and Ask #GetAgent to review financials, earnings expectations, and key levels before planning my trades. The Stock+ data made it much easier to understand both setups and prepare for different market scenarios.
I've also recorded a quick tutorial showing how to open a Stock+ account, deposit funds, and place your first trade.
Why I'm trading these events on Stock+:
✅ Trade US stocks 24/5, including after-hours earnings moves
✅ Works with SEC compliant brokers
✅ Buy fractional shares from just 0.0001 stock
✅ Deposit and trade on Stock+ to qualify for stock rewards
If you're looking at MU or $NVDA today, now is a great time to explore Stock+, do your research with Ask GetAgent, and position yourself before the volatility kicks in.
Recently, I was following the developments around Micron $MU earnings and Nvidia-related events so I ask #Getagent for a quick analysis. The AI infrastructure narrative remains one of the strongest themes in the market, with memory demand, AI chips, and data center spending continuing to attract investor attention. Nvidia closed FY2026 with revenue up more than 70% year-over-year, driven largely by AI demand, while investors remain focused on the next wave of AI hardware growth.
🔹 If $MU delivers strong guidance, AI memory stocks could continue benefiting from data center demand.
🔹 For $NVDA, I avoid chasing green candles and prefer waiting for pullbacks into support before adding exposure. The long-term AI story remains intact, but discipline matters.
What helped me most on Bitget Stock+:
✅ Overview section to quickly understand the company, valuation, and market position
✅ Financials section to review revenue growth, earnings trends, and profitability before making decisions
✅ 24/5 trading access so I can react to earnings and major announcements without waiting for regular market hours
Instead of trading based on headlines alone, Stock+ gives me the information needed to build a plan before entering a position.
#BitgetStockPlus #BitgetStocksUpgrade #MU #NVDA #AIStocks #Investing
强烈跟大家推荐非常好用的 GetAgent Playbook，目前已上线10种AI策略。入口就在你APP/Web端的 #GetAgent
点击自定义，跟 Agent 对话：杠杆改为10、哪些参数支持自定义等等。调整完成，点击启动—创建子帐户—划转资金，在有合适的信号后就能跟着 Playbook 开单了。
与跟单的区别：跟单只能跟着别人的策略走，Playbook 可以自定义修改 AI 策略参数，也可以创建自己的交易策略。
适合人群：没时间盯盘、容易情绪化交易、有策略想法但不想手动执行
以前，量化策略是专业团队的专利。现在，任何人都可以通过 GetAgent Playbook 创建、订阅、运行一个 AI 策略
> Still fine-tuning prompts for days?
> Got a trading idea but don’t know how to automate it?
> No idea where to start?
Bring your idea and let GetAgent Playbook do the heavy lifting> Still fine-tuning prompts for days?
> Got a trading idea but don’t know how to automate it?
> No idea where to start?
Bring your idea and let GetAgent Playbook do the heavy lifting> Still fine-tuning prompts for days?
> Got a trading idea but don’t know how to automate it?
> No idea where to start?
Bring your idea and let GetAgent Playbook do the heavy lifting.
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