wave8777

THE "CONSISTENTLY PROFITABLE" SKILL GAP & THE MYTH OF SUPPLEMENTAL INCOME FROM TRADING For many new traders or part-time traders, there is this pervasive belief that with some time and effort, they'll be able to make "just" a few grand per month to supplement their income. Or they "don't want to aim big, they just want to replace their current salary via trading so they can have more freedom." This is because people mistakenly believe that trading is like most other jobs, rather than it being a winner-take-all performance endeavor more akin to becoming a professional athlete. 99.9% of athletes will never make a dime professionally. There is no market demand for your average high school or college player. To even make league minimum in the NBA, you are still in the .0001% of basketball players. There is no such thing as just deciding to casually make a few grand as a pro athlete. Think about what it takes for someone to make $50k/yr as a golfer? The skill gap to earn an income or make the league minimum is crazy to comprehend. The analogy I gave with @AT09_Trader was the story of Brian Scalabrine. Even though Brian Scalabrine “sucked” in the NBA, he would absolutely annihilate 99.9% of the people calling him trash. He once said the famous line that he’s closer in skill to LeBron James than his haters are to him, and that line perfectly explains trading. The gap between unprofitable and elite looks massive from the outside, but the real canyon is between unprofitable and making any amount of money consistently. People look at a trader making $1M a year and think that’s a different species. They assume someone doing $100k a year is basically the same as the guy still blowing accounts, just with better luck. That’s like saying Scalabrine and your friend who plays pickup on Tuesdays are basically equal because neither is LeBron. Going from $0 to consistently profitable is the hardest jump in trading. You CANNOT just casually make a few grand per month or supplement income part-time. The skill level needed to consistently make ANY AMOUNT trading is the equivalent of being in the league. A trader who can pull $100k a year out of the market is not “kind of good.” They have competency in finding edge, executing trades, handling their psychology and risk management, and are competing in the league. From there, scaling to $300k, $500k, even $1M is usually a function of size, capital, and refinement, not a complete identity shift. But the trader still stuck at breakeven or red? They’re not one tweak away from $100k. They’re not “basically there.” They’re still trying to prove they belong on the court at all. The uncomfortable truth is this: the distance between $0 and $100k is far greater than the distance between $100k and $1M. One requires becoming a professional. The other simply just requires becoming a more refined one. My confidence in taking a trader from $100k to $1m is probably 10x higher than my confidence in taking a trader from $0 to $100k.

"Some of the best traders I know have been trading the same setup, on the same time frame, on the same market for 20 years. They don't care about anything else." That's from John Carter in "Mastering the Trade" and it's probably one of the more underrated messages in the book. We live in a culture that romanticizes variety. New strategies. New markets. New indicators. The idea of doing the same thing for twenty years sounds like a boring proposition to most people. But in trading, depth beats breadth more often than not. I went through a phase where I was trading momentum one week, mean reversion the next, and some other strategy the week after. I was learning a lot and making nothing. Because every time I switched, I reset my pattern recognition back to zero. I never got deep enough into any one approach to develop real feel for it. The turning point was when I committed to masterings 1-2 setups. 1-2 timeframes, which for me is the daily or the weekly. This in combination with 1-2 market conditions where I had an edge. And I just did that. Over and over. Although at first it felt limiting, with time it started feeling like mastery. I could suddenly see setups developing before they fully formed. I knew the failure patterns. I knew when to push and when to pass. That level of intuition only comes from deep repetition, not from knowing a little bit about a lot of things. Master one way of making money. Then, slowly, add a second. Then maybe a third. The traders who last decades tend to be specialists, not generalists.

me and Claude building an app at 3AM

Hard truth most traders still ignore: Skill alone won’t cut it anymore. Your nationality and place of residence will matter as much — if not more — than your edge. Taxes, security, rule of law, stability. Switzerland remains one of the very best anchors in an unstable world.

Switzerland’s Zug becomes bolt-hole for Gulf-based wealth ft.trib.al/7Jei6Sp

Japanese fan created a custom F1 engine replica using Red Bull cans 👏

Where would you rather be right now? 🌴 📍 Benidorm, Alicante, Spain 🇪🇸

In German, they don't say...!

A year from now, you won't remember most of this week's trades. You'll remember whether you stuck to your process or abandoned it under pressure. The individual trades fade. The habits you build during them don't. That's why process matters more than any single P&L screenshot.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Weekend thought. The version of you that reviews trades on a Saturday afternoon while everyone else is scrolling Twitter is the version that compounds over years. Nobody sees that work. Nobody applauds the quiet review session. But that's where the edge actually lives. In the boring hours where the spotlight is on you.

Alicante, Spain 🇪🇸

Trading statistics from 2019: - 25,243 trades - 1657 Trading Days - 294 red days - $4M in Losses 2015 started trading from Venezuela 2017 migrated legally to the US Since 2017 lived in rental apartments working my ass off every single day from 7am till 12am Monday-Monday. Moved 7 times People don’t believing in me, broke as fuc&, living away from my home… But with my vision INTACT and my commitment to my family and myself that I would CRUSH it with trading. (I still have this commitment and I am working harder than before because I am just getting started) That house @timothysykes shows is after ALL THAT How bad you want it?

This is not Italy! It's Thailand🇹🇭

📍 Cappadocia, Türkiye🇹🇷

Swezerland 🇨🇭

Fuji-san and a five-story pagoda in one frame. Japan didn't come to play. 📍Chureito Pagoda,Yamanashi,Japan 🇯🇵

Not the most famous lake in Italy. Arguably the most beautiful. 📍Limone sul Garda,Lake Garda,Italy 🇮🇹

Andrej Karpathy just dropped a project scoring every job in America on how likely an AI will replace it from 0-10 > Scraped all 342 occupations from the Bureau of Labor > Fed each one to an LLM with a detailed scoring rubric > Built an interactive treemap where rectangle size = number of jobs and color = how exposed that job is to AI The key signal in his scoring: if the work product is fundamentally digital and the job can be done entirely from a home office, exposure is inherently high. The scale: 0-1: Roofers, janitors 4-5: Nurses, retail, physicians 8-9: Software devs, paralegals, data analysts 10: Medical transcriptionists Average across all 342 occupations: 5.3/10. The entire pipeline is open source. BLS scraping, LLM scoring, the visualization. All of it. Much respect for the sensei this is scary and awesome