Quant (QNT)価格リアルタイムチャート、予測、時価総額、トークンアンロック、QNTコンセプト株 HQYC

BTC

$64,783.76

+1.50%

/QNT価格

Quant

QNT

No.91

$60.44

-1.85%

≈$60.44

時価総額

$899429640.16

流通時価

$729676284.72

流通量

12.07M

流通率

81.1266%

総供給量

14.88M

最大供給量

14.88M

24h取引額

$67267.64114088

24h取引量(BTC)

4.07M

24h換手率

0.557213%

市場シェア

0.03%

価格パフォーマンス

安

60.08

振幅

+0.03%

高

61.94

上場価格

$1.51

史上最高(2018-08-23)

$428.38

-85.89%

史上最低(2021-09-11)

$0.1636

+36837.22%

公式情報

契約

Ethereum: 0x4a22...254675

公式リンク

github

SNS

ネット情報

価格換算

チャート

市場

について

価格

時価総額

biểu đồ k-line

深度

1分

5分

15分

1時間

4時間

1日

TradingView

1時間

-0.58%

24時間

-1.85%

7日

-5.24%

30日

-7.26%

1年

-49.75%

すべて

+3.9K%

HQYC AI投資アシスタント

QNT下跌原因

QNT资金流向

QNT买卖支撑位

QNT多空比分析

QNT趋势分析

AI質問

QNT市場

CEX現物

CEXデリバティブ

ペア

#	解除日	解除数量	解除価値	総供給比	解除詳細
データなし

QNTについて

总部位于伦敦的 Quant Network 计划通过开发区块链操作系统 Overledger 来彻底改变区块链技术。该加密货币基于区块链技术运行。它是数字资产生态系统的重要组成部分。该项目专注于实际应用和采用。投资者在做出决定前应进行独立研究。市场状况和法规在不同司法管辖区存在差异。加密货币市场以高波动性著称。代币持有者通过投票机制参与网络治理。

66%

33%

強気

弱気

コミュニティ見解

Crypto GEMs 📈🚀

フォロー

Win $10,000 USDC – Zero Risk! Start with demo trading, then move to live trading. 👇 🔗 The Fraction AI Demo Trading Championship officially launches on August 3rd. Compete for a share of the prize pool over 7 days. You will use index trading. The Quant AI agent runs on the Hyperliquid platform, scanning the live market to provide you with high-confidence trading setups, complete with historical performance and backtesting data. You still need to make trading decisions, but no more guesswork. Register through our link to receive a $10 deposit matching bonus. Deposit $10 and get $10 of instant trading credit!

312

Binance

07-06 20:32

フォロー

New Stocks Listed Trade Stocks: ANTA, CBRS, DISK, FOTO, KMEM, QNT, and STRC 24/7 trading, 5 days a week. Zero commission, low platform fees, and over 7,000 stocks to choose from. Trade in one market, access the global market. Learn more 👉

456

Will

07-06 10:56

フォロー

Career advice for the AI era, from someone who has worked at Scale, OpenAI, and Google: 1/ AI can solve all problems with standard answers. School exams, LeetCode, system design—these are becoming AI's domain. What's truly valuable is the ability to write a loss function 🧭 2/ He turned down a higher-paying quant offer at Scale. Through Scale, he met a group of future founders and landed opportunities at DeepMind and OpenAI. His conclusion: Money is the least scarce resource. Time, connections, and reputation are the truly scarce commodities. 3/ His company no longer tests LeetCode in interviews. They test: If you're put in an unfamiliar environment, can you quickly identify a worthwhile problem to solve? Agents can solve complex problems, but only if someone first identifies which problem is worth solving. 4/ There's only one criterion for choosing a company: Is this company working on the most ambitious version of this problem? Do they really have a chance to succeed? AI has made ordinary software too easy; the real barrier lies in extreme focus on truly difficult problems. 5/ The last 10% is 90% of the value. The agent's output is average; the real value comes from that extra polishing, clean architecture, and creative details. He can tell directly in an interview who has spent this time and who hasn't. 6/ He rejected offers from Anthropic and Cursor in 2023. Looking back, he thinks the decision was correct, but he didn't gather enough information at the time. Anthropic's earliest product was a Slackbot, even worse than ChatGPT. 7/ AI won't replace all knowledge workers. Because choosing which problems are worth AI solving is inherently a human task. Are you practicing your problem-finding ability, or just your problem-solving skills?

481

CryptoQuant.com

07-02 15:51

フォロー

We are building the Vibe Quant community. This is a platform for traders to build strategies, alerts, and automate workflows using artificial intelligence and data. Paid API users will receive direct support from us. Tell us what data you need, and we'll respond quickly.

437

Ki Young Ju

07-02 14:08

フォロー

437

CryptoQuant.com

07-01 22:16

フォロー

Become a member of Vibe Quant. This community, dedicated to transforming on-chain data into AI trading agents, is now live ⤵️

417

CryptoQuant.com

06-23 20:19

フォロー

Gate.com network traffic heatmap shows altcoin fund inflow signals “Altcoins such as VGX, AMP, ZRX, SAND, QNT, YFI, YFF, CHZ, GHST, MANA, JASMY, and COMP are currently experiencing outflows exceeding inflows on the Gate.com platform.” – Author: @joao_wedson @joao_wedson Read more ⤵️

414

机灵的杰尼君🔶BNB

06-20 11:44

フォロー

I found an interesting website, Quiver Quant, that meticulously analyzes every single one of Trump's stock trades, even marking the buy and sell times (there's a delay). There were over 3600 trades in Q1 alone, averaging 30+ trades a day—more frequent than a full-time cryptocurrency trader! They must have a dedicated person managing it. Seriously, if I just copied their trades, wouldn't I be guaranteed to beat Buffett and Musk? 🥹

324

Bitstamp by Robinhood

06-19 19:32

フォロー

The following new assets are now available on the Bitstamp by Robinhood platform in the EU: $QNT (Quant) $ZRO (LayerZero) $FIGHT (FIGHT)

338

CryptoQuant.com

06-18 20:49

フォロー

CryptoQuant MCP provides data for Vibe Quants. However, LLM (Logical Leverage Model) is inherently prone to illusions and errors, especially when real money is involved. Fortunately, we have solutions 🧵 1. Human Involvement Let AI do the heavy lifting: → Acquire market data → Run calculations → Analyze signals → Generate trading setups But before any trade is executed, the results need to be reviewed and ultimately approved by humans. 2. System Hints Set strict system hints. For example: - Strictly follow the user's approach - Don't introduce new indicators - Don't make assumptions when data is missing - Only return the requested format - Maintain consistency for every analysis The better the system hints, the more predictable the output. 3. Good User Hints Don't ask, "Analyze the market, tell me what to buy." But be very specific 👇 - Define the data. - Define the rules. - Define the output. 4. Data Validation Layer Incorrect data = Incorrect trades. Before AI processes any data: - Validate API responses - Check for missing values - Validate timestamps - Detect outliers Never let the model infer based on corrupted input. 5. Rule-Based Safeguards Some decisions should never rely on AI. Hardcode key risk rules: - Maximum position size - Maximum leverage - Daily loss limit - Allowed trading pairs Even if AI makes mistakes, the system must not compromise risk management. 6. Logging and Explainability Every decision should leave a trace. → Market input → AI inference → Generated signals → User approval → Executed trades If a problem occurs, you will know exactly why. If you want to use CryptoQuant MCP as Vibe Quant, Please see below👇

392