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>>> OptimAI Agentic Trading // BUILD LOG #03 Research Agent ...... ✓ Online Strategy Agent ...... ✓ Online Backtesting Engine ......⚙️ Training Reasoning Engine ...... ⚙️ Optimizing Internal Testing ...... In Progress Release ...... Incoming >>> This week we've been improving our Trading Agent. It now researches ideas, generates...

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16,324 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 33

Фото профиля SAMADX
SAMADX1 месяц назад

«««OptimAI Agentic Trading // JOURNAL DE DÉVELOPPEMENT #03»»» Agent de Recherche ...... ✓ En ligne Agent de Stratégie ...... ✓ En ligne Moteur de Backtesting ...... ⚙️ En entraînement Moteur de Raisonnement ...... ⚙️ En optimisation Tests internes ...... En cours Lancement ...... Bientôt «««Cette semaine, l'équipe a continué d'améliorer son agent de trading.»»» Il est désormais capable de rechercher des opportunités, de générer des stratégies, de les tester via le backtesting et d'expliquer son raisonnement avant qu'une transaction ne soit exécutée. Le développement continue, mais chaque semaine nous rapproche d'une version encore plus performante. Rejoignez OptimAI dès maintenant : Mon canal Telegram :

Фото профиля BlockClaimed
BlockClaimed1 месяц назад

Backtested conviction always feels stronger than chasing random signals

Фото профиля Ella Tech & Tool
Ella Tech & Tool1 месяц назад

This progress is seriously exciting. Keep building

Фото профиля SAMADX
SAMADX1 месяц назад

Très belle progression. Le fait de combiner recherche, génération de stratégies, backtesting et raisonnement avant l'exécution des ordres montre une approche solide du trading assisté par l'IA. Hâte de découvrir les prochaines évolutions d'OptimAI.

Фото профиля Acee (✱,✱)
Acee (✱,✱)1 месяц назад

Keep cooking Team 💪

Фото профиля Md Monir Hossain Interlink 🚢
Md Monir Hossain Interlink 🚢1 месяц назад

Research Agent ...... ✓ Online Strategy Agent ...... ✓ Online Backtesting Engine ......⚙️ Training Reasoning Engine ...... ⚙️ Optimizing Internal Testing ...... In Progress Release ...... Incoming that's a massive

Фото профиля ValenHai
ValenHai1 месяц назад

nice

Фото профиля Jennifer Davies
Jennifer Davies1 месяц назад

Can't wait to set this up so an AI agent can lose all my money on autopilot

Фото профиля Любовь
Любовь1 месяц назад

Imagine this: your trading agent researches, tests, and explains everything BEFORE the trade. OptimAI Agentic Trading makes this a reality, freeing you from routine tasks. We’re building the future. #OptimAI #AgentAI

Фото профиля Raven Thesis
Raven Thesis1 месяц назад

long-awaited; eagerly anticipated for a long time

Фото профиля Ejiknice
Ejiknice1 месяц назад

wow this is intresting

Фото профиля Fauzi Rahmat
Fauzi Rahmat1 месяц назад

Good news

Фото профиля Pirko
Pirko1 месяц назад

lfg optimAI

Фото профиля Hunny
Hunny1 месяц назад

Cant wait

Фото профиля lego.🐬TermMax
lego.🐬TermMax1 месяц назад

가보자

Фото профиля Airdrop Link
Airdrop Link1 месяц назад

When Tge

Фото профиля Shahin bogura2026
Shahin bogura20261 месяц назад

I know every body like this project

Фото профиля Jack
Jack1 месяц назад

Nice

Фото профиля Ajay Kumar Sharma $TOR
Ajay Kumar Sharma $TOR1 месяц назад

Hello optimal network team please help

Фото профиля Darman
Darman1 месяц назад

LFG

Фото профиля CHAN MIA
CHAN MIA1 месяц назад

Optimal

Фото профиля nurulamin
nurulamin1 месяц назад

Hi

Фото профиля Hidayat0214
Hidayat02141 месяц назад

Let's do it

Фото профиля The Lucky Journey 🍀
The Lucky Journey 🍀1 месяц назад

Sounds like you're making solid progress, curious to see how the backtesting engine turns out

Фото профиля Broke/Uncle
Broke/Uncle1 месяц назад

Keep building

Фото профиля Văn Toán
Văn Toán1 месяц назад

Great

Фото профиля coin68
coin681 месяц назад

tuyệt vời

Фото профиля magbi lakew
magbi lakew1 месяц назад

good

Фото профиля PurchAA
PurchAA1 месяц назад

Good

Фото профиля Tobyfan❤️❤️
Tobyfan❤️❤️1 месяц назад

Keep building

Фото профиля GHS1971
GHS19711 месяц назад

Nice moment

Фото профиля Asih $TOR
Asih $TOR1 месяц назад

morning

Фото профиля Joss 🤖ボッ
Joss 🤖ボッ1 месяц назад

⬛️⬛️⬛️⬛️⬛️⬛️⬛️⬛️ GM Crypto Fam' and #Airdrop Hunters 📱 Join @OptimaiNetwork and start earning passive Rewards ! My Referral Link : My Referral Code for @OptimaiNetwork : DA373F01 ⬛️⬛️⬛️⬛️⬛️⬛️⬛️⬛️

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Building a backtesting engine that never stops searching. 24/7. Next-gen Backtesting Engine - AUTO MODE update: → Claude scans the web: Reddit, forums, trading communities & research → Grok scans X for new trading concepts and setups → Both feed fresh strategy ideas directly into the platform → The engine automatically converts them into backtests → Profitable ideas get identified → Bad ideas get rejected → Then the search continues. Some strategies work. Most don't. And that's exactly the point. Instead of manually hunting for the next edge, I'm building a system that can search → test → reject → discover → repeat… 24/7. Still building. Still improving. But this is getting seriously interesting. Would you let an AI research engine search for your next trading edge while you sleep?
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One of our engineers built Errand, our open-source take on Grok Bot and Muse, and wrote up how it came together. One developer, one week. The app is ~4k lines of TS. Every agent still gets its own machine with a shell, a browser, and a filesystem that survives between tasks. He never wrote a provisioner or an event log infra. If you're about to build this layer yourself, that's what Runta is. Week one goes into your product instead of a provisioner. I'd like to hear where you're stuck, if you're in the middle of it right now.

Guanlan Dai

101,333 просмотров • 11 дней назад

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.
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Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

225,653 просмотров • 2 месяцев назад

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:
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Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

118,297 просмотров • 11 месяцев назад

your ai agent tested 1,262 trading strategies overnight. by morning it had forgotten every one of them. so tonight it tests some of them again. next week, again. everything it learned is sitting in a log file that nothing reads. every run starts on the same blank page. that's why it keeps handing you the idea that already died in a crash, with a new name on it, and you pay to find out twice. ai agent already does everything a fund does. finds the idea, writes the code, tests it against years of history, puts it live, reads what went wrong afterwards. all of it runs tonight for nothing. the one thing it can't do is remember. what got tried, what failed, in which market. nobody has built that part yet. that's the whole gap. not better models. a notebook. whoever builds it first gets a system that improves every week without them touching it. read the article below and save it. it's the full guide to how an ai trading agent is built, and how to make it remember every strategy it has already tested.
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your ai agent tested 1,262 trading strategies overnight. by morning it had forgotten every one of them. so tonight it tests some of them again. next week, again. everything it learned is sitting in a log file that nothing reads. every run starts on the same blank page. that's why it keeps handing you the idea that already died in a crash, with a new name on it, and you pay to find out twice. ai agent already does everything a fund does. finds the idea, writes the code, tests it against years of history, puts it live, reads what went wrong afterwards. all of it runs tonight for nothing. the one thing it can't do is remember. what got tried, what failed, in which market. nobody has built that part yet. that's the whole gap. not better models. a notebook. whoever builds it first gets a system that improves every week without them touching it. read the article below and save it. it's the full guide to how an ai trading agent is built, and how to make it remember every strategy it has already tested.

cvxv666

48,965 просмотров • 1 месяц назад

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---
1:15

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

echo.hive

88,611 просмотров • 1 год назад

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.
2:10

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.

Aish

630,651 просмотров • 2 лет назад