正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

3 trading modes. completely different workflows. AI Trading, Signal Flip and Manual are all available in NeuroTrader AI. I’ve started checking the mode first because each one has its own controls and limits. Especially useful when switching between research and execution.

Strike Labs

59,100 subscribers

11,228 次观看 • 1 个月前 •via X (Twitter)

科学技术财经

Anya Rossi• Live Now

Private livecam show

2 条评论

Strike Labs 的头像

Strike Labs1 个月前

Took me a minute to realise how much the mode changes the workflow:

Md Anowar Hossen 的头像

Md Anowar Hossen1 个月前

@neurotraderai_ what is your photo market plan

相关视频

BREAKING: AI Trading Agents LIVE on Mode Trade Outperform the market and simple buy & hold strategies with AI Trading Agents built for speed, consistency and precision: ✅ Autonomous execution ✅ Emotion-free decisions ✅ Real-time AI signal detection Create your own now 🧵👇

BREAKING: AI Trading Agents LIVE on Mode Trade Outperform the market and simple buy & hold strategies with AI Trading Agents built for speed, consistency and precision: ✅ Autonomous execution ✅ Emotion-free decisions ✅ Real-time AI signal detection Create your own now 🧵👇

Mode 🟡

28,342 次观看 • 1 年前

6 months ago I was running 4 monitors, 3 tabs of options chains. Missing entries because I was too slow switching between research and broker. Every "AI trading tool" I tried made the research faster and ignored the execution completely. Then I connected Perplexity to the Public API via Agent skill. Here's whats the setup looks like 🧵

6 months ago I was running 4 monitors, 3 tabs of options chains. Missing entries because I was too slow switching between research and broker. Every "AI trading tool" I tried made the research faster and ignored the execution completely. Then I connected Perplexity to the Public API via Agent skill. Here's whats the setup looks like 🧵

Matthew@Public

53,028 次观看 • 6 个月前

🚀 New on Bluprint Claim 1,000 Virtual USDT and experience AI trading — completely risk-free. 📈 Test different AI Trading Agents 🤖 Explore AI-powered strategies 💰 No real funds required Log in and start trading 👇

🚀 New on Bluprint Claim 1,000 Virtual USDT and experience AI trading — completely risk-free. 📈 Test different AI Trading Agents 🤖 Explore AI-powered strategies 💰 No real funds required Log in and start trading 👇

Bluwhale

30,654 次观看 • 4 个月前

AI Screener. Mobile Edition. A first preview. The full feature set comes next. Charts. Transactions. Project data. Trending. Social Intelligence. Trading. All in one place. LITE and PRO modes will both be available. Your crypto screener, now in your pocket. Data and Social Intelligence are already built in. Up next: agentic trading systems. ethereum:0x9cf0ed013e67db12ca3af8e7506fe401aa14dad6

AI Screener. Mobile Edition. A first preview. The full feature set comes next. Charts. Transactions. Project data. Trending. Social Intelligence. Trading. All in one place. LITE and PRO modes will both be available. Your crypto screener, now in your pocket. Data and Social Intelligence are already built in. Up next: agentic trading systems. ethereum:0x9cf0ed013e67db12ca3af8e7506fe401aa14dad6

SPECTRE AI

19,866 次观看 • 3 个月前

Jane Street engineer: "95% of Jane Street Quants are running swarms of AI agents We build JaneGPT in 3 days - our own AI we use for trading and research to make $39.6B/year" In 1-hour Jane Street team explained how they uses AI for their fund - and how they build their own AI that generates profit 5:14 → how AI lives inside Jane Street's 20:22 → AI agents working on Jane Street's big data research 45:26 → why trading firm builds its own data-centers 1:10:05 → AI that watches live metrics and helps when things break Learn how Jane Streaet use AI - no matter what, this is the best AI + trading conversation out there:

Jane Street engineer: "95% of Jane Street Quants are running swarms of AI agents We build JaneGPT in 3 days - our own AI we use for trading and research to make $39.6B/year" In 1-hour Jane Street team explained how they uses AI for their fund - and how they build their own AI that generates profit 5:14 → how AI lives inside Jane Street's 20:22 → AI agents working on Jane Street's big data research 45:26 → why trading firm builds its own data-centers 1:10:05 → AI that watches live metrics and helps when things break Learn how Jane Streaet use AI - no matter what, this is the best AI + trading conversation out there:

bodila

77,563 次观看 • 3 天前

“Most people already have more intelligence than they know what to do with.” iArb | 裁定取引 joins the Insilico Terminal Podcast to talk about finding obscure crypto edges, building trading systems, and putting AI to work. We covered the Tari arbitrage, oil perps on Hyperliquid, automating research, owning compute, and where he sees the next opportunities for small teams. 00:00 Introducing Arb and his first crypto arbitrage 09:25 Oil perps: trading the market rules 16:43 Bitcoin, hardware and the AI opportunity 26:40 Where AI value accrues 33:26 Why small teams can move faster 39:08 AI for trading research and automation 45:51 How to think in systems 51:07 Manual trading and AI-built systems 55:38 AI workflows and what changes next

“Most people already have more intelligence than they know what to do with.” iArb | 裁定取引 joins the Insilico Terminal Podcast to talk about finding obscure crypto edges, building trading systems, and putting AI to work. We covered the Tari arbitrage, oil perps on Hyperliquid, automating research, owning compute, and where he sees the next opportunities for small teams. 00:00 Introducing Arb and his first crypto arbitrage 09:25 Oil perps: trading the market rules 16:43 Bitcoin, hardware and the AI opportunity 26:40 Where AI value accrues 33:26 Why small teams can move faster 39:08 AI for trading research and automation 45:51 How to think in systems 51:07 Manual trading and AI-built systems 55:38 AI workflows and what changes next

Insilico Terminal

22,370 次观看 • 1 个月前

The TradeHost AI from allows Betfair traders to use our AI for speed ratings, form, course, draw and many other criteria. These parameters are then ranked in order by the AI and this creates a total rating number for each horse running every single day 24/7. This can be especially useful for beginners or those who don't have the time or expertise to develop their own trading strategies for Betfair. The TradeHost AI will find value - the most important factor for trading and betting on Betfair - without members having to do any research. We call it trading with a view. Most Betfair traders that don't trade with a view will lose money - in running and pre race! Copy trading using the TradeHost AI can help Betfair traders diversify their portfolios, reduce risk, and potentially increase Betfair profits by following the strategies of the successful JuiceStorm automated trading portfolio that has used AI on Betfair since 2021.

The TradeHost AI from allows Betfair traders to use our AI for speed ratings, form, course, draw and many other criteria. These parameters are then ranked in order by the AI and this creates a total rating number for each horse running every single day 24/7. This can be especially useful for beginners or those who don't have the time or expertise to develop their own trading strategies for Betfair. The TradeHost AI will find value - the most important factor for trading and betting on Betfair - without members having to do any research. We call it trading with a view. Most Betfair traders that don't trade with a view will lose money - in running and pre race! Copy trading using the TradeHost AI can help Betfair traders diversify their portfolios, reduce risk, and potentially increase Betfair profits by following the strategies of the successful JuiceStorm automated trading portfolio that has used AI on Betfair since 2021.

Betfair Trading - JuiceStorm.com

29,329 次观看 • 2 年前

8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.

8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.

TeddyCEO

163,404 次观看 • 1 个月前

I’ve already covered the Character Economy before with @charmsai This time, we’re looking at Eunice Agents and the ongoing $5,000 $EUNICE trading competition on Charms. AI, community, trading, and onchain incentives all in one ecosystem thanks to @EuniceCharms What are your thoughts??

I’ve already covered the Character Economy before with @charmsai This time, we’re looking at Eunice Agents and the ongoing $5,000 $EUNICE trading competition on Charms. AI, community, trading, and onchain incentives all in one ecosystem thanks to @EuniceCharms What are your thoughts??

Qinggg 👑 || CTNG HOUSE

39,390 次观看 • 5 个月前

Trading shouldn’t stop at the signal. BLEEEP × Lighter brings verifiable trading intelligence and onchain execution together. Using Lighter’s trading data, the BLEEEP Agent supports backtesting and AI strategy analysis, turning validated ideas into controlled Perp DEX orders. Users remain in control through strategy rules, risk limits, and explicit approvals. Private keys remain secured in a vault on the user’s device, with transactions signed locally. Lighter delivers fast execution through custom ZK infrastructure that proves exchange operations, with proofs and state updates verified on Robinhood Crypto chain. BLEEEP proves the decision. Lighter proves the execution.

Trading shouldn’t stop at the signal. BLEEEP × Lighter brings verifiable trading intelligence and onchain execution together. Using Lighter’s trading data, the BLEEEP Agent supports backtesting and AI strategy analysis, turning validated ideas into controlled Perp DEX orders. Users remain in control through strategy rules, risk limits, and explicit approvals. Private keys remain secured in a vault on the user’s device, with transactions signed locally. Lighter delivers fast execution through custom ZK infrastructure that proves exchange operations, with proofs and state updates verified on Robinhood Crypto chain. BLEEEP proves the decision. Lighter proves the execution.

BLEEEP

26,050 次观看 • 2 个月前

🚨 Genspark launched AI Inbox, AI Sheet 2.0 and Teams collaboration. This Silicon Valley startup is taking on the tech giants with the first true all-in-one AI workspace. No more switching between 10 different tools. 6 wild examples:

🚨 Genspark launched AI Inbox, AI Sheet 2.0 and Teams collaboration. This Silicon Valley startup is taking on the tech giants with the first true all-in-one AI workspace. No more switching between 10 different tools. 6 wild examples:

Alvaro Cintas

13,431 次观看 • 11 个月前

THIS APP JUST REPLACED MY ENTIRE TRADING SETUP i used to open 15+ tabs every morning just to research one position Minara puts research, analysis, backtesting, and execution in one AI desk here's what it actually does.

THIS APP JUST REPLACED MY ENTIRE TRADING SETUP i used to open 15+ tabs every morning just to research one position Minara puts research, analysis, backtesting, and execution in one AI desk here's what it actually does.

liam.

42,496 次观看 • 1 个月前

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 次观看 • 11 个月前

This trader made $132,356 on Polymarket using trading bot, buying markets for $11 His bot trades 5-minute crypto Up/Down markets and combines high-frequency directional trading with dynamic hedging Its strategy is simple: > Finds mispricing with its probability model and buys the outcome where it sees an edge > Increases the position if the signal is confirmed, and partially reduces exposure when the signal weakens > When the underlying asset reverses, it buys the opposite side as a hedge His Polymarket nickname: trinity42 You can build your own trading bot here: (Free trial available after registration) Using its strategy and high-quality execution, this guy turned $2,000 into $132,356 in 4 months of trading

This trader made $132,356 on Polymarket using trading bot, buying markets for $11 His bot trades 5-minute crypto Up/Down markets and combines high-frequency directional trading with dynamic hedging Its strategy is simple: > Finds mispricing with its probability model and buys the outcome where it sees an edge > Increases the position if the signal is confirmed, and partially reduces exposure when the signal weakens > When the underlying asset reverses, it buys the opposite side as a hedge His Polymarket nickname: trinity42 You can build your own trading bot here: (Free trial available after registration) Using its strategy and high-quality execution, this guy turned $2,000 into $132,356 in 4 months of trading

VALIX

50,580 次观看 • 2 个月前

Let FlashX simplify your trading journey with 1-Click 👉Install now: 📈Spot and Perp Trading (up to 50X) 🗣️ AI and KOL insights 📄 All info in 1 place 🚀 CEX like trading in the Vault (limit orders) 👥Referral programs ⛓️ Multi chain trading 🔥 Super fast execution #Trading #FlashX

Let FlashX simplify your trading journey with 1-Click 👉Install now: 📈Spot and Perp Trading (up to 50X) 🗣️ AI and KOL insights 📄 All info in 1 place 🚀 CEX like trading in the Vault (limit orders) 👥Referral programs ⛓️ Multi chain trading 🔥 Super fast execution #Trading #FlashX

FlashX

34,314 次观看 • 1 年前

48 wins. 2 losses. in one day. But here’s what impressed me: My AI agent took the 2 losses, analyzed them, and rewrote its own strategy. It’s not just trading. It’s learning. At what point do you think AI agents will become the default trading tool for all traders?

48 wins. 2 losses. in one day. But here’s what impressed me: My AI agent took the 2 losses, analyzed them, and rewrote its own strategy. It’s not just trading. It’s learning. At what point do you think AI agents will become the default trading tool for all traders?

Sharbel

85,915 次观看 • 9 个月前

🚨 Genspark launches All-in-One AI Workspace 2.0. No more switching between 20 different tools, and they just hit $100M ARR in 9 months, becoming the first AI agent unicorn. 5 wild examples: 1. Download all papers from a LinkedIn post

🚨 Genspark launches All-in-One AI Workspace 2.0. No more switching between 20 different tools, and they just hit $100M ARR in 9 months, becoming the first AI agent unicorn. 5 wild examples: 1. Download all papers from a LinkedIn post

Alvaro Cintas

21,637 次观看 • 9 个月前

“AI trading will likely come before AI payments.” — CZ 🔶 BNB at EASY Residency S4 Demo Day During his fireside with Ella Zhang, CZ explained that payments aren’t the immediate bottleneck for most AI users -- they can still pay with their own cards. Trading is different. It demands real-time analysis, faster decisions and constant execution, making human intervention increasingly inefficient. Rather than AI companies moving into payments first, crypto players may lead by bringing AI into trading.

“AI trading will likely come before AI payments.” — CZ 🔶 BNB at EASY Residency S4 Demo Day During his fireside with Ella Zhang, CZ explained that payments aren’t the immediate bottleneck for most AI users -- they can still pay with their own cards. Trading is different. It demands real-time analysis, faster decisions and constant execution, making human intervention increasingly inefficient. Rather than AI companies moving into payments first, crypto players may lead by bringing AI into trading.

YZi Labs

38,033 次观看 • 2 个月前