ALEX_BingX

15 posts

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ALEX_BingX

ALEX_BingX

@ALEX2BTC

Connecting KOLs, traders & growth in Web3 🌍 TG:@Alex2BTC

Katılım Mayıs 2026
10 Takip Edilen6 Takipçiler
Owais
Owais@OwaisAlpha1·
Can MEXC / Blofin / BingX BD contact ?
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Blacx| |Trader full time ที่ตามหางานประจำ
Fibo ที่ตีโดย Ai ก็เเม่นใช้ได้เลยนะครับ อีกหน่อยถ้ามีเงินเติม pro หรือบริหารtoken ดีๆ คนทั่วไปเริ่มต้นใหม่กับคนศึกษาเทคนิคอลกราฟก็คงไม่ต่างกันมากละ เคลียร์เเนวเเล้ว เเนวต่อไปอยู่ข้างล่างเเทน
Blacx| |Trader full time ที่ตามหางานประจำ tweet media
Blacx| |Trader full time ที่ตามหางานประจำ@tonnuhm123

tradingview Remix ก็ดีนะสั่งAI วาด Fibonacci และแนวรับ/แนวต้านบนกราฟใหฟ้เราเองได้ด้วย เเน่นอนว่าไม่เห็นด้วยก็ตามไปลบได้

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Pepo
Pepo@Pepotothemooon·
HIP-3 volume ส่วนมาก เกิดตอนที่ตลาดปกติปิด คิดเองมาตลอดว่า ตอนตลาดเปิดมันจะเยอะกว่า
Pepo tweet media
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
For merchants, communities, OTC desks, or anyone moving meaningful volume: If you'd like to set up an affiliate account and explore the numbers, feel free to DM me or reach out on Telegram: @Alex2BTC
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
A thought for crypto P2P merchants: If you're onboarding users every day but don't have your own exchange affiliate structure in place, there's a good chance you're leaving a substantial amount of money on the table. Think about it. The users who buy USDT from you don't just hold it. They trade. Every $100M in trading volume can generate roughly $40000–50000 in fees. Through a proper affiliate setup, a large portion of those fees can be shared back. Now multiply that by the volume generated from your regular clients and larger traders over a month. Many merchants focus on the spread. The smartest ones capture the downstream trading revenue too. Run the numbers. You'll understand why some merchants are making far more than their quoted spreads.
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Trader Steve 🐺
Trader Steve 🐺@TraderSteve_·
Thank you for 6,000 followers!! Love you all ❤️
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
Most trading KOLs I speak to are leaving a significant amount of money on the table every month without realizing it. As a simple reference: for every ~$10M in trading volume, around ~$5,000 in fees is generated — and in most cases, that value is not optimized or captured properly by the KOL or their community. If you're consistently generating volume (equities / commodities / crypto), your fee structure is often the biggest hidden PnL leak in your system. I work with selected high-volume KOLs to restructure this into a more efficient setup inside a top-tier exchange ecosystem — not just rebates, but long-term aligned revenue sharing. This is not open for everyone. I only take on active volume-driven partners. If you're doing meaningful numbers, DM me.
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
I’m ALEX,BD of BingX exchange.Actively seeking high-quality Web3 KOLs for BingX partnerships.We offer full exchange-level support and the market’s top rebate rates.If you’re a KOL or crypto creator, DM me to discuss opportunities. ☺️🤝
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F25
F25@F25_RR·
$AAL A stock that you must monitor....
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
@Sol_CJ888 Good example of why flexibility matters. Did the accumulation model become obvious to you immediately after the failed distribution?
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CJ⚡️🪽
CJ⚡️🪽@Sol_CJ888·
bitcoin:native I took a Wyckoff Model 1 Distribution trade on Bitcoin to start the week, and it did not go exactly as planned. The setup came after seeing price get an ugly Model 1 Re-Accumulation (AR on the chart) that then started deviating the high, putting in new potential Wyckoff distribution points. What I really liked about the model was the SMT with ETH. That, combined with seeing supply take over at the high, I shorted targeting the Wyckoff low. I wanted the demand created by the AR not to cause model failure. Many times we can get this, but in this instance, it caused the model to fail. I knew it had room for error, so I did have my SL to BE. I also traded the accumulation Model that spawned in that demand, which ended up being the only trade I made money on in this range. Overall, I felt good about the entry and being open to the market if it wanted to complete this schematic, but ultimately, price had other ideas to take out the model and then complete the original target. It is what it is, and we move on to the next more XP ⚡️
CJ⚡️🪽 tweet media
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Crypto Connoisseur
Crypto Connoisseur@Themanlibs·
Small piece on the 618ers discord. I joined a month ago to see what it is all about. I wanted to learn some LTF stuff, some psychology. And I can say: I am blown away! I cannot describe the value packed in this discord. There are legit 5 Full time traders executing trades round the clock. Always there to answer any and all Q's Livestreams by each of them packed with alpha day in, day out. @Albert_618 - is there to teach you critical thinking, owns the DAX @AlxWlf11 - Master Range/channel trader @Trader_Vantage - MAster at LTF BTC scalping using MS, S&D @kssb__ - There to show you how to use the 1s TF and to have 12000000000000RR trades lol, NQ whisperer @materagian - Psychology, bang trade after trade after trade on MNQ There is nothing like this discord. 5 in 1. Legit Don't fade.
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Hippo
Hippo@Hippo_Trade·
SILVER: We played the retest of the inverse H&S, aligned with liquidity and Fibonacci confluences. Asia open provided strong continuation in our favor. TP1 has been secured at a predefined target level. Planned in advance, not random. Price is now continuing toward our final target around 6R. The full setup was shared beforehand inside the HippoTrades Telegram, with the underlying framework and execution principles explained in the book ‘Trading Without Hope’. hippotrades.com #Silver #XAGUSD #Trading #Forex #PriceAction #Liquidity #RiskManagement #Fibonacci #TradingPsychology #Metals
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ALEX_BingX
ALEX_BingX@ALEX2BTC·
@Sol_CJ888 Filtering bad trades is honestly one of the hardest skills to develop. Good breakdown 👾
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CJ⚡️🪽
CJ⚡️🪽@Sol_CJ888·
My next Wyckoff Case Study is on AAVE 👾 In this video, I go over a failed model that I did not take for the reasons outlined in the video. I thought it was a great example of when market structure does not support the model, and when volume does not support the model. I believe part of ascending in trading is filtering out the bad trades, hurting your win rate. This is why I made this case study, looking at the factors that can help us filter out lower probability models. Let me know your thoughts and how you are seeing it ⚡️ youtu.be/tmmpvzMs628
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Alexander
Alexander@altuscapra·
Me when I share MT knowledge with a friend and he thinks he’s a genius after 2 days of studying.
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