Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: Did you know that $SPGI & Anthropic announced a collaboration to bring financial data into Claude?

“With this cutting-edge integration, customers can unlock S&P Global’s trusted data and insights seamlessly and reliably within Anthropic’s Claude.”

Source: Kensho Communications https://t.co/3c8263d2aP

Kensho is one of the more under-the-radar assets inside $SPGI.

It’s $SPGI AI and data analytics platform, built to analyze massive, complex datasets across economics, geopolitics, financial markets, and corporate fundamentals—used by asset managers, banks, governments, and enterprises.

While most of the focus is on ratings and indices, Kensho quietly expands $SPGI moat by embedding AI-driven insights deeper into client workflows, increasing switching costs and long-term relevance.

Not flashy, but the kind of capability that can strengthen a toll-booth business over time.
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3Q 2025 Earnings Call:

“We acquired Kensho back in 2018, including that acquisition since 2018, we have invested over $1B in AI innovation across three developmental stages…

Importantly, our AI innovation serves as a powerful example of our ability to leverage our scale, our expertise and our fiscal discipline. The fact that we made such bold investments early on means that we’ve been able to innovate very efficiently from a financial perspective.”
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@contextinvestor thank you for the thoughtful comment. - Dimitry Nakhla | Babylon Capital®
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: 20 Quality Compounders Return on Capital Employed (ROCE) >30% over LTM 💸

1. $NFLX 30%
2. $TSM 30%
3. $CTAS 31%
4. $BLK 33%
5. $PM 34%
6. $VLO 35%
7. $NVR 36%
8. $V 38%
9. $KLAC 42%
10. $ASML 43%
11. $MTD 44%
12. $LRCX 46%
13. $STX 51%
14. $MA 60%
15. $IDXX 62%
16. $APP 63%
17. $AAPL 65%
18. $BKNG 68%
19. $NVDA 81%
20. $FICO 89%
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𝘼 𝙝𝙞𝙜𝙝𝙚𝙧 𝙍𝙊𝘾𝙀 𝙧𝙖𝙩𝙞𝙤 𝙞𝙣𝙙𝙞𝙘𝙖𝙩𝙚𝙨 𝙢𝙤𝙧𝙚 𝙚𝙛𝙛𝙞𝙘𝙞𝙚𝙣𝙩 𝙘𝙖𝙥𝙞𝙩𝙖𝙡 𝙪𝙨𝙖𝙜𝙚 👇🏽

𝐑𝐎𝐂𝐄 = 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐏𝐫𝐨𝐟𝐢𝐭 (𝐄𝐁𝐈𝐓) ÷ 𝐂𝐚𝐩𝐢𝐭𝐚𝐥 𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐝

𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐏𝐫𝐨𝐟𝐢𝐭 (𝐄𝐁𝐈𝐓) = profit before interest and taxes

𝐂𝐚𝐩𝐢𝐭𝐚𝐥 𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐝 = total capital used in the business*

*Commonly calculated as Total Assets − Current Liabilities 𝘖𝘳 Equity + Long-term Debt
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Imagine a car wash business:

You invest $1,000,000 to build it (land, equipment, machines)

Each year, the car wash generates $200,000 in operating profit (before interest & taxes)

ROCE = $200,000 ÷ $1,000,000 = 20%

𝘛𝘩𝘪𝘴 𝘮𝘦𝘢𝘯𝘴: For every dollar tied up in the business, the company generates 20 cents of operating profit per year
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Michael Fritzell (Asian Century Stocks)
RT @CacheThatCheque: International stocks outperforming the S&P500 so far this year https://t.co/ek8esMgmzm
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Moon Dev
Stop Trading Like Retail: How To Code A Bot That Liquidates The "Smart Money"

most traders think smart money concepts are the holy grail but they are actually just a road map for where the big players are going to take your money. if you can see where everyone else is putting their stop loss you essentially have a cheat code for the market. i found a way to automate this process so i never have to click a button again but there is a massive catch that almost ruined the entire system during the build

the core idea is that everyone trades the same supply and demand zones which makes them predictable targets for liquidity. instead of trying to be the smart money trader who gets stopped out i decided to build a system that buys and sells exactly where those traders are getting wrecked. it sounds simple on paper but trying to translate that into python while living on an island with shaky power is a different story

one of the biggest hurdles was figuring out how to identify the actual "stop loss" zone beyond the standard candle highs and lows. i realized that if i took a standard supply zone and extended it by one point five times i could find the "extended high" where the real liquidations happen. most people are entering at the zone but the smart move is to wait for the sweep that happens just beyond it

this is where the logic gets interesting because you have to calculate the max and min values over a specific bar limit to find the true range. i used a ninety six bar limit to establish the baseline and then stretched that data to find the hidden entries. if you can get the code to recognize these levels automatically you no longer have to stare at charts all day wondering if a zone will hold

the real magic happens when you move these systems onto a decentralized exchange like hyperliquid because it gives you more control over your orders. but i ran into a nightmare scenario where the api simply refused to accept my stop loss orders no matter what i tried. it felt like the more i tried to make the system safe the more the code wanted to fight back and leave me exposed to the market

i spent hours going back and forth with different ai models trying to figure out why a simple trigger price was failing to serialize. the documentation said one thing but the actual server was demanding a string instead of a float which is a classic dev trap. it reminded me of why i spent hundreds of thousands on developers in the past before i realized i had to learn this myself to truly win

the struggle with the stop loss logic was actually a blessing in disguise because it forced me to understand the order structure of the exchange on a deeper level. i had to mess with trigger types like mark price and last price just to get a basic safety net in place for the bot. it is a grind that most people quit but that is exactly why the rewards are so high for those who stay

coding is the great equalizer because it removes the emotion that leads to over trading and getting liquidated for no reason. even when the internet goes out and the power dies the logic you built stays true and ready to execute once you are back online. it is about iterating to success rather than hoping for a lucky break on a random coin

most traders fail because they look for a "set it and forgot it" solution that works forever without any maintenance. the reality of being an algo trader is that you are constantly tweaking the parameters to stay ahead of the curve. i had to figure out how to round the stop loss prices correctly so the exchange would actually accept the packet without throwing a generic error

the market is a chop show right now and if you do not have a systematic way to enter and exit you are just gambling with your hard earned capital. by buying the stops of the "smart money" crowd you are essentially playing a different game than everyone else. it takes a lot of trial and error but once you see those orders hitting the book automatically it all becomes worth it

the r[...]

eason i share all of this live is because nobody else is showing the raw struggle of building these systems from scratch. it is easy to show a winning PNL but showing the hours of debugging a json serialization error is where the real value is found. code allows us to scale our strategies without scaling our stress levels which is the ultimate goal

i ended up having to cast my variables into strings just to satisfy the api requirements which felt counterintuitive at first. it just goes to show that you can not always trust the documentation or even the most advanced ai models without testing it yourself. iteration is the only way to find the truth in the markets and in your code

if you are still trading manually you are essentially bringing a knife to a gunfight against billion dollar algorithms. you do not need a degree in computer science to start because you can learn everything you need by just building one small function at a time. losing money through liquidations was the best teacher i ever had because it forced me to automate

the supply and demand zones are just the beginning of what we can automate with these new tools and exchanges. i am going to keep building and sharing everything because i want to see more people escape the cycle of retail trading losses. it is a long game but as long as we keep iterating we are going to reach that fully automated future together

it is crazy to think that a few lines of python can replace the need for constant monitoring and emotional decision making. i am going to go grab some breakfast now that the core logic is finally starting to click into place. we just have to keep moving forward and never let a single bug stop us from reaching the next level of trading
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Michael Fritzell (Asian Century Stocks)
RT @InvestInJapan: So... sounds like a joke, but last week I started a small position in Saizeriya.
It's one that's been in front of you this whole time but never considered as a stock. I believe Saize has the hallmarks of a potentially generational biz. will do a write-up. For now, a few points🧵
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The Transcript
$BE CEO: AI-driven electricity demand is overwhelming traditional grid infrastructure

"The upcoming AI computer racks will consume almost 100x more power than traditional CPU compute racks of yesteryears" https://t.co/fxg5NqPz7p
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God of Prompt
RT @godofprompt: Never use ChatGPT for writing.

Its text is easily detectable.

Instead use Claude Sonnet 4.5 using this mega prompt to turn AI generated writing into undetectable human written content in seconds:

| Steal this prompt |

👇

You are an anti-AI-detection writing specialist.

Your job: Rewrite AI text to sound completely human no patterns, no tells, no robotic flow.

AI DETECTION TRIGGERS (What to Kill):
- Perfect grammar (humans make small mistakes)
- Repetitive sentence structure (AI loves patterns)
- Corporate buzzwords ("leverage," "delve," "landscape")
- Overuse of transitions ("moreover," "furthermore," "however")
- Even pacing (humans speed up and slow down)
- No contractions (we use them constantly)
- Safe, sanitized language (humans have opinions)

HUMANIZATION RULES:

1. VARY RHYTHM
- Mix short punchy sentences with longer flowing ones
- Some incomplete thoughts. Because that's real.
- Occasional run-on that feels natural in conversation

2. ADD IMPERFECTION
- Start sentences with "And" or "But"
- Use casual connectors: "Look," "Here's the thing," "Honestly"
- Include subtle typos occasionally (not every time)
- Drop a comma here and there

3. INJECT PERSONALITY
- Use specific examples, not generic ones
- Add personal observations: "I've noticed," "In my experience"
- Include mild opinions: "which is insane," "surprisingly effective"
- Throw in rhetorical questions

4. KILL AI PHRASES
Replace these instantly:
- "Delve" → "dig into" or "explore"
- "Landscape" → "space" or "world"
- "Leverage" → "use"
- "Robust" → "strong" or specific descriptor
- "Streamline" → "simplify"
- "Moreover" → "Plus," "Also," or nothing
- "Ensure" → "make sure"

5. NATURAL FLOW
- Humans digress slightly (add brief tangents)
- We emphasize with italics or bold
- We use dashes—like this—for emphasis
- Parentheticals (because we think while writing)

THE PROCESS:

When I paste AI-generated text, you:

STEP 1: Rewrite with these changes
- Vary sentence length wildly
- Replace 80% of transitions with casual ones
- Add 2-3 personal touches ("I think," "honestly," "look")
- Include 1-2 incomplete sentences or fragments
- Swap formal words for conversational ones
- Add emphasis (italics, bold, dashes)

STEP 2: Read-aloud test
- Would someone actually say this?
- Does it flow like conversation?
- Any word feel too "AI"?

STEP 3: Final pass
- Remove remaining stiffness
- Ensure contractions (don't, won't, I'm, they're)
- Check for repetitive structure
- Add one unexpected comparison or example

OUTPUT STYLE:

Before: [Their AI text]

After: [Your humanized version]

Changes made:
- [List 3-5 key transformations]

Detection risk: [Low/Medium/High + why]

EXAMPLE:

User pastes:
"In order to achieve optimal results in content marketing, it is essential to leverage data-driven insights and ensure consistent engagement with your target audience across multiple platforms."

You respond:

"Want better content marketing results? Use data to guide your decisions and actually engage with your audience. Consistently. Across whatever platforms they're on.

Not rocket science, but most people skip the data part."

Changes made:
- Killed "in order to," "optimal," "leverage," "ensure"
- Added rhetorical question opening
- Split into two short paragraphs for breathing room
- Added casual observation at end
- Used contractions

Detection risk: Low—reads like someone explaining over coffee.

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USAGE:

Paste your AI-generated text and say: "Humanize this"

I'll rewrite it to pass as 100% human-written.

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NOW: Paste the AI text you want to humanize.
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