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Dexter now runs on real-time market data. I added the core data stack to make it even more powerful. Data sources: • SEC filings • financial statements • stock prices • key metrics Now Dexter can handle more complex questions like: “Is MSFT’s revenue growth justifying its P/E ratio?... show more

Virat Singh

47,887 subscribers

48,368 次观看 • 8 个月前 •via X (Twitter)

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The new Financial Metrics API is live. Covers 30,000 tickers over 30+ years. Metrics include: • profitability metrics • efficiency metrics • valuation metrics • liquidity metrics • growth metrics All data can be accessed over annual, quarterly, and trailing 12 months periods. Profitability metrics: includes net margin, operating margin, return on equity, and more. Efficiency metrics: includes inventory turnover, days sales outstanding, operating cycle, and more. Valuation metrics: includes price to earnings, free cash flow yield, PEG growth, and more. Liquidity metrics: includes current ratio, quick ratio, operating cash flow ratio, and more. Growth metrics: includes revenue growth, earnings growth, free cash flow growth, and more. I add new metrics regularly. Let me know if you have any requests.

The new Financial Metrics API is live. Covers 30,000 tickers over 30+ years. Metrics include: • profitability metrics • efficiency metrics • valuation metrics • liquidity metrics • growth metrics All data can be accessed over annual, quarterly, and trailing 12 months periods. Profitability metrics: includes net margin, operating margin, return on equity, and more. Efficiency metrics: includes inventory turnover, days sales outstanding, operating cycle, and more. Valuation metrics: includes price to earnings, free cash flow yield, PEG growth, and more. Liquidity metrics: includes current ratio, quick ratio, operating cash flow ratio, and more. Growth metrics: includes revenue growth, earnings growth, free cash flow growth, and more. I add new metrics regularly. Let me know if you have any requests.

Virat Singh

30,674 次观看 • 1 年前

Dexter now writes investment memos. My flow: I pick a stock and ask Dexter to pull all data + learn everything about it. Then Dexter gives me variant takes and bull / bear on the stock. Total time is ~3 mins. I went from idea → long $DDOG at $109.

Dexter now writes investment memos. My flow: I pick a stock and ask Dexter to pull all data + learn everything about it. Then Dexter gives me variant takes and bull / bear on the stock. Total time is ~3 mins. I went from idea → long $DDOG at $109.

Virat Singh

16,038 次观看 • 1 个月前

Dexter vs. Claude Code I ran tests overnight and Dexter came out ahead on complex financial tasks that required deep research. Dexter won on: • speed (by 92%) • cost (by 26%) • correctness (by 31%) I use Claude Code often, so this was fun to see. A key challenge for CC is that it relies on web search for financial data. Most of what it finds comes from news sites, blogs, and other secondary sources. Dexter uses primary source data from Financial Datasets, so the performance gap makes sense. Plenty of room to improve on Dexter. The gap will only grow from here. Evals from vals. Report coming next.

Dexter vs. Claude Code I ran tests overnight and Dexter came out ahead on complex financial tasks that required deep research. Dexter won on: • speed (by 92%) • cost (by 26%) • correctness (by 31%) I use Claude Code often, so this was fun to see. A key challenge for CC is that it relies on web search for financial data. Most of what it finds comes from news sites, blogs, and other secondary sources. Dexter uses primary source data from Financial Datasets, so the performance gap makes sense. Plenty of room to improve on Dexter. The gap will only grow from here. Evals from vals. Report coming next.

virat

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Braden Dennis 📊

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The global industry standard oracle platform, Chainlink, is now live on the Injective Mainnet. Time to market for dApp developers is critical and now with Injective’s EVM deployment, the iBuild AI dApp creator, onchain financial modules, and Chainlink data streams - developers on Injective can experience one of the fastest time to market anywhere in the industry. Chainlink brings real time Data Streams with sub-second latency to the only blockchain purpose-built for finance. This now gives Injective developers full access to Chainlink’s battle-tested data stream markets across the Injective ecosystem. Helix 🧬 will be the first Injective dApp to integrate Chainlink Data Streams set to power its crypto and RWA markets. Benefits for using Chainlink Data Streams on Injective: ✅ Low latency market data: up to sub-second delivery ✅ Institutional reliability: collaborating with institutional data giants like ICE and FTSE Russell, An LSEG Business ✅ Programmability: custom tailor formats, cadence, and fields to match a dApps exact needs. Injective markets now powered by Chainlink Data Streams. Ninjas are just getting started. 🥷

Injective 🥷

116,813 次观看 • 7 个月前

A new look for the new era of data movement Over the past 12+ months we've been focused on building out mump2p, our Ethereum data propagation product, onboarding top validators to test it with, and educating our peers on the merits of decentralized coding. Now with the launch of mump2p on Ethereum mainnet firmly in our sights, it feels like the right time to give Optimum's visual identity a refresh-- something to match the sleek, powerful data acceleration network we're deploying. The advent of RLNC powered data movement frees blockchains from the networking bottleneck, so they can perform at the pace demanded by our ever-expanding digital economy. The new era begins soon with data propagation on Ethereum, more chains and use cases to follow. Time to raise the ceiling for blockchains, and do it in style.

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Optimum

10,636 次观看 • 3 个月前

Registration is now LIVE for GTM25 – the can’t miss go-to-market event of the year! 🎉 We’re kicking things off with a virtual summit — then hitting the road to bring Go-To-Market Intelligence to New York, Boston, San Francisco, and London. Here’s what to expect: • Keynotes – Discover how GTM Intelligence can ignite your revenue growth • Breakout Sessions – Learn new GTM strategies and put AI into action • Networking – Connect with top leaders and expand your GTM playbook Plus, be the first to hear major product announcements and exclusive industry insights shaping the future of GTM. Don’t miss your chance to see what’s next in data, AI, and go-to-market. Register now: #GTM25 #GTMIntelligence

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ZoomInfo

27,197 次观看 • 1 年前

Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.

Made $100K+ by building a simulation that predicts SPX price movements before they happen. Fed 40+ years of SPX trading history into MiroFish (18k GitHub stars) and let AI reverse engineer every pattern. Now runs dozens of profitable trades. I have the exact step-by-step guide to build your own SPX prediction system. Giving it free for 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can DM you) What you will learn: ✅ How to pull live SPX data with Alpha Vantage and Quandl ✅ Building a Python data pipeline that runs 24/7 ✅ Feature engineering (RSI, MACD, custom signals) ✅ Loading historical data into MiroFish simulator ✅ Multi-agent setup (macro strategist, earnings analyst, sentiment analyst) ✅ Running probability forecasts across market scenarios ✅ Trading logic for ES futures and SPY ETF ✅ Backtesting your model on real market history This is not prediction. This is loading decades of market data and letting AI find the patterns humans missed. Most traders guess. This system runs simulations. Comment "Money" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.

Himanshu Kumar

75,907 次观看 • 4 个月前

Thanks to all GCV community pioneers passion and support! GCV mall now is number #1 either Weekly, Monthly or 2023! Now passed 60,000 five star ⭐ and 120,000 likes! Rate is 4.9! I know a lot pioneers want to see cars iPhone ect high value products. But merchant want to see market stable price to put their products on the mall! So when we support GCV price and call this price on social media is to provide data to these merchants for their consideration. If they evaluate the price can be accepted by more merchant and most pioneers accept this price, it will help them post their price and provide more valuable products. But we can't jump to high value products without these small items. So let's united together to provide more data! This is my personal analysis just for reference! #WhatIDoForPi #PiDayGCV

Thanks to all GCV community pioneers passion and support! GCV mall now is number #1 either Weekly, Monthly or 2023! Now passed 60,000 five star ⭐ and 120,000 likes! Rate is 4.9! I know a lot pioneers want to see cars iPhone ect high value products. But merchant want to see market stable price to put their products on the mall! So when we support GCV price and call this price on social media is to provide data to these merchants for their consideration. If they evaluate the price can be accepted by more merchant and most pioneers accept this price, it will help them post their price and provide more valuable products. But we can't jump to high value products without these small items. So let's united together to provide more data! This is my personal analysis just for reference! #WhatIDoForPi #PiDayGCV

Doris Yin 东方紫莲🪷

32,192 次观看 • 3 年前

🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.

🚨 THE BIGGEST BOTTLENECK IN AI ISN'T COMPUTING POWER ANYMORE IT'S MOVING DATA. Instead of laying new cables, Chinese researchers have upgraded existing fiber infrastructure by doing two things at once: Using three wavelength bands (C + L + S) instead of the usual two. Using four cores inside each fiber instead of one. Each core acts like an independent highway, and each band acts like an extra lane on that highway. Together, they’ve reportedly increased transmission capacity per core by nearly 50% and overall data throughput by up to 5×. This matters enormously for AI. Modern AI clusters move terabits of data per second between thousands of GPUs. The biggest bottleneck is often not the chips themselves, but moving data fast enough between them. If you can push 5× more data through the same physical cables, you can train bigger models faster and reduce network congestion. Why this is significant: • It shows multi-core + extended spectrum technology moving from labs into real-world commercial use • The system has already run over 35 km of existing telecom network • It could be especially useful for submarine cables and large-scale data center interconnects • China is also eyeing it for its “Eastern Data, Western Computing” project The deeper implication: We’re reaching the physical limits of how much data we can push through single-core fibers using traditional methods. By combining spatial multiplexing (multiple cores) with spectral multiplexing (more wavelength bands), engineers are finding new ways to keep scaling bandwidth without having to dig up the planet to lay new cables. This kind of breakthrough is quiet but foundational it’s the kind of infrastructure upgrade that will determine how fast AI and cloud computing can actually grow in the coming years. The future of data movement might not require more cables. It might just require smarter ones. How important do you think multi-core and multi-band fiber will be for keeping up with AI’s exploding data demands? Follow for more frontier networking, photonics, and infrastructure technology.

TheNewPhysics

20,485 次观看 • 1 个月前

This Huawei ad I just walked by at Munich airport hit me like a ton of bricks. Huawei watch? HUAWEI HEALTH? Delivering your intimate, real-time location, behavioral & physiological data (heart rate??) directly to the CCP is absolutely insane. I hadn't even considered how many of these watches must be on people hands right now, and how powerful a tool of espionage (the next-level invasive data gathering!) the CCP has at its disposal. Grok, what countries have the biggest usage per capita of huawei watch? Also, considering what I wrote earlier in this post, and given that Huawei is bound by Chinese law to share all data it has access to with the the Chinese government (and not disclose it is doing so), what impact is there for personal privacy and security for users of Huawei watch?

This Huawei ad I just walked by at Munich airport hit me like a ton of bricks. Huawei watch? HUAWEI HEALTH? Delivering your intimate, real-time location, behavioral & physiological data (heart rate??) directly to the CCP is absolutely insane. I hadn't even considered how many of these watches must be on people hands right now, and how powerful a tool of espionage (the next-level invasive data gathering!) the CCP has at its disposal. Grok, what countries have the biggest usage per capita of huawei watch? Also, considering what I wrote earlier in this post, and given that Huawei is bound by Chinese law to share all data it has access to with the the Chinese government (and not disclose it is doing so), what impact is there for personal privacy and security for users of Huawei watch?

Jan Jekielek

149,648 次观看 • 1 年前

What if checking crypto derivatives data was as easy as sending a text? That's exactly what our traders need in fast-moving markets - so they built it. Today, we're launching FalconX MarketsBot – the internal tool that has been essential to our trading team and is now available to all on Telegram. MarketsBot gives you: ✓ Aggregated metrics: vols, basis, funding, skews, and more ✓ Instant visualizations: Raw metrics transformed into charts and summary tables ✓ Real-time insights: Delivered in Telegram, optimized for mobile Ready to try it? Search @ FalconX_Markets_DM_Bot on Telegram or go to

What if checking crypto derivatives data was as easy as sending a text? That's exactly what our traders need in fast-moving markets - so they built it. Today, we're launching FalconX MarketsBot – the internal tool that has been essential to our trading team and is now available to all on Telegram. MarketsBot gives you: ✓ Aggregated metrics: vols, basis, funding, skews, and more ✓ Instant visualizations: Raw metrics transformed into charts and summary tables ✓ Real-time insights: Delivered in Telegram, optimized for mobile Ready to try it? Search @ FalconX_Markets_DM_Bot on Telegram or go to

FalconX

506,208 次观看 • 1 年前

What if crypto research was as easy as chatting with ChatGPT, but powered by real market data👑 Introducing CMC AI, a powerful new tool from CoinMarketCap that combines the speed of AI with the depth of live crypto data. It delivers fast, data-backed answers to your questions: ✅ Want to know why Bitcoin's price is rising? ✅ Curious about the latest news on your favorite cryptocurrency? ✅ Need sentiment analysis? It pulls real-time data and explains it in seconds. But it goes far beyond basic Q&A. In the future, you’ll be able to ask anything! For example, you could ask it to: – Discover undervalued tokens based on volume, MC, and sentiment. – Compare Layer 1s or L2s by adoption, speed, and dev activity. – Detect rug-pull risk via wallet distribution and tokenomics red flags. – Break down your portfolio by risk, correlation, and potential return. – Explore new use cases in DeFi, AI, RWA and DePIN And much more! 🔗Try it here: CMC AI changes how you learn, think, and act in Web3🧠

What if crypto research was as easy as chatting with ChatGPT, but powered by real market data👑 Introducing CMC AI, a powerful new tool from CoinMarketCap that combines the speed of AI with the depth of live crypto data. It delivers fast, data-backed answers to your questions: ✅ Want to know why Bitcoin's price is rising? ✅ Curious about the latest news on your favorite cryptocurrency? ✅ Need sentiment analysis? It pulls real-time data and explains it in seconds. But it goes far beyond basic Q&A. In the future, you’ll be able to ask anything! For example, you could ask it to: – Discover undervalued tokens based on volume, MC, and sentiment. – Compare Layer 1s or L2s by adoption, speed, and dev activity. – Detect rug-pull risk via wallet distribution and tokenomics red flags. – Break down your portfolio by risk, correlation, and potential return. – Explore new use cases in DeFi, AI, RWA and DePIN And much more! 🔗Try it here: CMC AI changes how you learn, think, and act in Web3🧠

Alaoui Capital

34,889 次观看 • 1 年前

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

cvxv666

2,400,134 次观看 • 4 个月前

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

Sudo su

60,445 次观看 • 1 个月前

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 次观看 • 1 年前