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the bad boy of algo trading | ceo @ blackrose

3k following1k followers

The Entrepreneur

Vihan Singh is the fearless 'bad boy of algo trading' and CEO of Blackrose, India's rising AI powerhouse. He combines cutting-edge AI insights with practical algo trading mastery, turning complex ideas into robust, real-world trading systems. With a knack for building communities and a relentless hustle, he’s not just riding the AI wave – he’s shaping it.

Impressions
746-656
$0.13
Likes
4-1
80%
Retweets
0
0%
Replies
0
0%
Bookmarks
1-1
20%

For a self-proclaimed ‘bad boy of algo trading,’ Vihan’s more like the charming crypto nerd who’s still debugging his love life algorithms. Maybe it’s time to let Claude Code help with a ‘how to manage crushes’ script next?

Vihan’s biggest win so far is pioneering a transparent, iterative AI trading workflow that not only works in theory but has been backtested successfully on real market data — all while building Blackrose into one of India’s largest AI-focused startups.

Vihan’s life purpose centers around revolutionizing financial markets through AI and algorithmic trading, while simultaneously building India’s largest AI-driven conglomerate. He’s here to disrupt, innovate, and empower a new generation of tech-savvy traders and founders.

He believes in the power of AI as an enabler, not a magic bullet, emphasizing critical thinking and iterative learning. Vihan values transparency, technical rigor, and the entrepreneurial spirit of turning bold ideas into actionable businesses that create real impact.

His key strengths include deep technical expertise in AI and algo trading, entrepreneurial vision, and the ability to communicate complex workflows clearly to engage and educate his audience. His hands-on approach to building and iterating trading systems ensures practical relevance and credibility.

Vihan’s ambitious drive and multiple roles might lead to spreading his focus too thin, possibly causing slower scaling of audience engagement if not balanced well. Also, his 'bad boy' persona might alienate more conservative or traditional audiences looking for polished professionalism.

To grow his audience on X, Vihan should leverage his unique voice by sharing more bite-sized, visually engaging content that demystifies AI-driven trading for beginners. Engaging directly with AI and trading communities via threads, polls, and collaborations with other thought leaders can boost visibility and follower loyalty.

Fun fact: Vihan personally uses a multi-step workflow involving ChatGPT and Claude Code to design, code, audit, and refine AI-driven trading systems, proving that behind the ‘bad boy’ image lies a methodical and data-driven entrepreneur.

Top tweets of Vihan Singh

Getting more and more tradable systems designed by LLMs. This is what I’ve learned actually works for me. 👇 Step-by-step breakdown of the process that turns AI ideas into real systems I can paper trade. 1/ I start in ChatGPT (o3 model). I use a custom bio where I describe myself as an experienced trader—this primes it into “expert mode.” Then I share a picture from a real trading session and describe the context. In this case: intraday breakout from a volatility “squeeze.” I explain the type of data I have + what kind of realistic performance I expect (after slippage & fees). Then I ask GPT to brainstorm and rank strategies by: - difficulty to implement - likelihood of working live 2/ That prompt usually delivers surprisingly good ideas. I pick the one that makes the most sense intuitively. Then I ask GPT to: - write a full, detailed trading plan - create step-by-step implementation instructions for a programmer to code it into a backtester 3/ These instructions go into Claude Code (my coding agent). Claude takes the logic and turns it into Python. I run the code, but only on strict In-Sample data. This first version is rarely perfect—but that’s okay. 4/ Then I ask Claude to: - show screenshots of the last 10 trades - plot all entries & exits I use prompts like “think deeply” or “audit the logic for bias.” After some iterations, I ask Claude to summarize the implemented logic in plain English. This is key for the next step. 5/ I paste the summarized logic back into ChatGPT. GPT often spots errors, edge cases, or logical holes. Its feedback then goes back to Claude for code fixes. 6/ Once we’re closer to a clean build, I run a full audit: - Entries - Exits - Stops - Position sizing - Indicators Claude checks each piece individually, not all at once. Why? LLMs are more accurate on small, focused tasks. 7/ When code is solid, I test Out-of-Sample and on different markets. The equity curve I shared is from QQQ on full history - The system was created only on a few years of SPY. That’s an important check against overfitting. 8/ The whole process took me nearly a day. Not “automagic,” but powerful. Yes, I might still find issues in paper trading—that’s part of the game. But every iteration trains me (and the LLM) what to look for next time. 9/ Is this system overfit? I don’t think so. The core logic is dead simple—but I probably wouldn’t have thought of it myself. Core idea: - Detects tight ranges (volatility “squeeze”) - Trades breakouts with stop, profit target, and EOD exit Clean, robust, and intuitive. 10/ Conclusion? This workflow is absolutely worth trying. But treat LLMs as junior analysts, not black-box gods. Use critical thinking. Stress-test every idea. The more experience you bring, the more value you’ll get from it.

653

You don't need a PhD to learn AI. These 20 X // Twitter accounts make it easy for you: 1. @mreflow Top AI YouTuber, runs Future Tools and co-hosts The Next Wave podcast. 2. @OfficialLoganK Leads Gemini API at Google, key figure in humanizing Google AI products. 3. @MatthewBerman Breaks AI news at lightning speed with high-quality YouTube videos. 4. @RubenHssd Top LinkedIn AI creator with 600k+ followers, creator of easygen.io. 5. @LiorOnAI Tech-savvy voice in AI media, runs the AlphaSignal newsletter. 6. @matanSF & @EnoReyes Co-founders of Factory AI (aka the “Devin killer”). 7. @JesseTinsley Big thinker in AI infrastructure — making major moves behind the scenes. 8. @Borriss Funny, sharp AI commentator known for hilarious Twitter hooks. 9. @sriramk Senior AI Advisor to the White House, ex-A16Z, former podcast host. 10. @danshipper Runs Every, teaches AI-assisted writing, and builds AI-powered tools. 11. @Scobleizer OG tech media guy, always early to spot new trends and startups. 12. @DhravyaShah 19-year-old prodigy, founder of Supermemory.ai (9k+ GitHub stars). 13. @Altimor Founder of GetLindy — powerful AI agent for meetings, LinkedIn, etc. 14. @pvncher Creator of RepoPrompt, top AI coding tool; podcast guest on The Next Wave. 15. @gregisenberg Host of Startup Ideas podcast, founder of Late Checkout. 16. @nico_jeannen Indie hacker turned founder, now building AI ad-creation tools. 17. @bilawal (Bilawal Sidhu) Ex-Google, TED AI host, dubbed the “Mark Rober of AI SFX”. 18. @iruletheworldmo Possibly an insider, posts entertaining and eerily accurate AI leaks. 19. @rowancheung Founder of The Rundown (1M+ subs), now also runs Rundown University. 20. @vihan13singh Founder of Blackrose, building India's largest AI conglomerate.

594

Profiles like this are getting $100M offers from Meta. Let me show you the EXACT career path that’s worth more than most IPOs: The Journey: • 2010-2012: Intern at Microsoft Research Asia (2 years) • 2014: 4-month research intern at Microsoft • 2017: PhD from MIT in Computer Science • 2017-2023: Staff Research Scientist at Google DeepMind (6+ years) • 2023-Present: Member of Technical Staff at OpenAI Translation: This person has been building the AI systems that are reshaping civilization. What makes someone worth $100M? 🔥 Deep expertise in foundational AI research 🔥 Experience at the 3 most important AI labs on Earth 🔥 PhD from the world’s top CS program 🔥 6+ years at DeepMind during the breakthrough era 🔥 Currently at OpenAI during the ChatGPT revolution The brutal reality: While most people argue about whether AI will take jobs, this person IS the AI revolution. Career lessons hidden in plain sight: ❌ “Follow your passion” → This person followed the hardest problems ❌ “Work-life balance” → This person worked at the epicenter of history ❌ “Job security” → This person built skills so rare they’re worth $100M ❌ “Climb the ladder”* → This person jumped between the world’s best labs The $100M reality: - Total compensation packages: Up to $100M over 4 years - Annual value: $25M+ per year for top talent - Base salaries: $2M+ confirmed - Stock/equity: The majority of the package For context: This is more than most Fortune 500 CEOs make. More than most hedge fund managers. More than most professional athletes. The uncomfortable truth: AI researchers are becoming the new athletes. The top 50 people in AI are worth more than entire companies. What this person probably knows: • How large language models actually work • The secret sauce behind ChatGPT/GPT-4 • Google’s next-generation AI strategies • The technical details that determine AI’s future Market reality: There are maybe 500 people on Earth with this specific combination of skills. Meta is offering compensation packages worth up to $100M to poach OpenAI’s top researchers. The new career math: Traditional path: 40 years × $200K = $8M lifetime earnings AI research path: 4 years × $25M = $100M Questions for your career: • Are you building skills that can’t be replicated? • Are you working on problems that matter? • Are you positioning yourself at the center of major shifts? The bigger picture: We’re watching the birth of a new aristocracy. The people who understand AI will own the future. Everyone else will rent it from them. My prediction: In 10 years, this salary will look cheap. Because AI researchers aren’t just employees. They’re the architects of the next economy.

528

Most engaged tweets of Vihan Singh

You don't need a PhD to learn AI. These 20 X // Twitter accounts make it easy for you: 1. @mreflow Top AI YouTuber, runs Future Tools and co-hosts The Next Wave podcast. 2. @OfficialLoganK Leads Gemini API at Google, key figure in humanizing Google AI products. 3. @MatthewBerman Breaks AI news at lightning speed with high-quality YouTube videos. 4. @RubenHssd Top LinkedIn AI creator with 600k+ followers, creator of easygen.io. 5. @LiorOnAI Tech-savvy voice in AI media, runs the AlphaSignal newsletter. 6. @matanSF & @EnoReyes Co-founders of Factory AI (aka the “Devin killer”). 7. @JesseTinsley Big thinker in AI infrastructure — making major moves behind the scenes. 8. @Borriss Funny, sharp AI commentator known for hilarious Twitter hooks. 9. @sriramk Senior AI Advisor to the White House, ex-A16Z, former podcast host. 10. @danshipper Runs Every, teaches AI-assisted writing, and builds AI-powered tools. 11. @Scobleizer OG tech media guy, always early to spot new trends and startups. 12. @DhravyaShah 19-year-old prodigy, founder of Supermemory.ai (9k+ GitHub stars). 13. @Altimor Founder of GetLindy — powerful AI agent for meetings, LinkedIn, etc. 14. @pvncher Creator of RepoPrompt, top AI coding tool; podcast guest on The Next Wave. 15. @gregisenberg Host of Startup Ideas podcast, founder of Late Checkout. 16. @nico_jeannen Indie hacker turned founder, now building AI ad-creation tools. 17. @bilawal (Bilawal Sidhu) Ex-Google, TED AI host, dubbed the “Mark Rober of AI SFX”. 18. @iruletheworldmo Possibly an insider, posts entertaining and eerily accurate AI leaks. 19. @rowancheung Founder of The Rundown (1M+ subs), now also runs Rundown University. 20. @vihan13singh Founder of Blackrose, building India's largest AI conglomerate.

594

Profiles like this are getting $100M offers from Meta. Let me show you the EXACT career path that’s worth more than most IPOs: The Journey: • 2010-2012: Intern at Microsoft Research Asia (2 years) • 2014: 4-month research intern at Microsoft • 2017: PhD from MIT in Computer Science • 2017-2023: Staff Research Scientist at Google DeepMind (6+ years) • 2023-Present: Member of Technical Staff at OpenAI Translation: This person has been building the AI systems that are reshaping civilization. What makes someone worth $100M? 🔥 Deep expertise in foundational AI research 🔥 Experience at the 3 most important AI labs on Earth 🔥 PhD from the world’s top CS program 🔥 6+ years at DeepMind during the breakthrough era 🔥 Currently at OpenAI during the ChatGPT revolution The brutal reality: While most people argue about whether AI will take jobs, this person IS the AI revolution. Career lessons hidden in plain sight: ❌ “Follow your passion” → This person followed the hardest problems ❌ “Work-life balance” → This person worked at the epicenter of history ❌ “Job security” → This person built skills so rare they’re worth $100M ❌ “Climb the ladder”* → This person jumped between the world’s best labs The $100M reality: - Total compensation packages: Up to $100M over 4 years - Annual value: $25M+ per year for top talent - Base salaries: $2M+ confirmed - Stock/equity: The majority of the package For context: This is more than most Fortune 500 CEOs make. More than most hedge fund managers. More than most professional athletes. The uncomfortable truth: AI researchers are becoming the new athletes. The top 50 people in AI are worth more than entire companies. What this person probably knows: • How large language models actually work • The secret sauce behind ChatGPT/GPT-4 • Google’s next-generation AI strategies • The technical details that determine AI’s future Market reality: There are maybe 500 people on Earth with this specific combination of skills. Meta is offering compensation packages worth up to $100M to poach OpenAI’s top researchers. The new career math: Traditional path: 40 years × $200K = $8M lifetime earnings AI research path: 4 years × $25M = $100M Questions for your career: • Are you building skills that can’t be replicated? • Are you working on problems that matter? • Are you positioning yourself at the center of major shifts? The bigger picture: We’re watching the birth of a new aristocracy. The people who understand AI will own the future. Everyone else will rent it from them. My prediction: In 10 years, this salary will look cheap. Because AI researchers aren’t just employees. They’re the architects of the next economy.

528

Getting more and more tradable systems designed by LLMs. This is what I’ve learned actually works for me. 👇 Step-by-step breakdown of the process that turns AI ideas into real systems I can paper trade. 1/ I start in ChatGPT (o3 model). I use a custom bio where I describe myself as an experienced trader—this primes it into “expert mode.” Then I share a picture from a real trading session and describe the context. In this case: intraday breakout from a volatility “squeeze.” I explain the type of data I have + what kind of realistic performance I expect (after slippage & fees). Then I ask GPT to brainstorm and rank strategies by: - difficulty to implement - likelihood of working live 2/ That prompt usually delivers surprisingly good ideas. I pick the one that makes the most sense intuitively. Then I ask GPT to: - write a full, detailed trading plan - create step-by-step implementation instructions for a programmer to code it into a backtester 3/ These instructions go into Claude Code (my coding agent). Claude takes the logic and turns it into Python. I run the code, but only on strict In-Sample data. This first version is rarely perfect—but that’s okay. 4/ Then I ask Claude to: - show screenshots of the last 10 trades - plot all entries & exits I use prompts like “think deeply” or “audit the logic for bias.” After some iterations, I ask Claude to summarize the implemented logic in plain English. This is key for the next step. 5/ I paste the summarized logic back into ChatGPT. GPT often spots errors, edge cases, or logical holes. Its feedback then goes back to Claude for code fixes. 6/ Once we’re closer to a clean build, I run a full audit: - Entries - Exits - Stops - Position sizing - Indicators Claude checks each piece individually, not all at once. Why? LLMs are more accurate on small, focused tasks. 7/ When code is solid, I test Out-of-Sample and on different markets. The equity curve I shared is from QQQ on full history - The system was created only on a few years of SPY. That’s an important check against overfitting. 8/ The whole process took me nearly a day. Not “automagic,” but powerful. Yes, I might still find issues in paper trading—that’s part of the game. But every iteration trains me (and the LLM) what to look for next time. 9/ Is this system overfit? I don’t think so. The core logic is dead simple—but I probably wouldn’t have thought of it myself. Core idea: - Detects tight ranges (volatility “squeeze”) - Trades breakouts with stop, profit target, and EOD exit Clean, robust, and intuitive. 10/ Conclusion? This workflow is absolutely worth trying. But treat LLMs as junior analysts, not black-box gods. Use critical thinking. Stress-test every idea. The more experience you bring, the more value you’ll get from it.

653
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