zostaff
@zostaff • 14,361 subscribers
can't play me, i wrote the rules
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Michael Lewis, author of The Big Short and Moneyball, says 160 million dollars a day is being skimmed off the top of the U.S. stock market. After the book came out, the FBI opened a tip line for Wall Street traders. It's taken by HFT firms. Computers that are milliseconds faster than you. You decide to buy Microsoft. They see your order before the exchange does. They buy first. They sell it back to you at a higher price.
zostaff1,008,614 просмотров • 12 дней назад
CLAUDE MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,503 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Claude took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. Started with $1, then $5, then $10, then $50, then $2,503 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.
zostaff1,941,309 просмотров • 2 месяцев назад
AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.
zostaff1,070,893 просмотров • 1 месяц назад
The founder of Renaissance Technologies - the hedge fund that makes 66% a year, runs the most secretive trading floor on Earth, and has never accepted outside money - once stood in front of 500 mathematicians and explained exactly how he did it. Jim Simons. The Einstein Lecture. American Mathematical Society. 1 hour 20 minutes.They left it on a small university channel. Almost nobody knows it exists. No one's talking about it. Bookmark it before they do. Then read the article below.
zostaff528,031 просмотров • 1 месяц назад
Bill Perkins went from a nobody to running his own hedge fund. He started on the exchange floor as a clerk's trainee in 1991. Bad grades, cut from the football team, an electrical engineering degree. Today he runs a 500 million dollar energy hedge fund. And plays poker at a professional level on the side. He was once fired from his own friend's fund. He once came close to going to zero. WSJ asks him about the strategy he calls the lazy guy strategy.
zostaff141,010 просмотров • 10 дней назад
Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw. The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each. 1. Jane Street, janestreet/magic-trace 5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction. 2. Goldman Sachs, goldmansachs/gs-quant Derivative pricing the GS traders use at their desks. MIT licensed. 3. JP Morgan, finos/perspective What JPM traders use to watch markets in real time. A $24k/year terminal, for free. 4. BlackRock, blackrock/lcso Rust optimizer for portfolio problems. Where scipy gives up, this works. 5. Hudson River Trading, hudson-trading/corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms. 6. Two Sigma, twosigma/flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. 7. D.E. Shaw, deshaw/pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. Bookmarked it
zostaff288,428 просмотров • 22 дней назад
The MIT professor who trains quants for Citadel, Two Sigma, and Renaissance just gave a closed-door keynote at Oxford in front of Man Group's $151B team. They left the recording on a public server. Probably by accident. 1 hour. Free. No one's talking about it. Bookmark it before Oxford notices. Then read the article below.
zostaff379,988 просмотров • 1 месяц назад
20 years ago Jane Street's entire compute cluster was six Dell boxes stacked on the floor at the end of an office row. They called it the Hive. Last week they let Dwarkesh Patel walk through their new Texas data center. 4,032 GPUs. Each rack pulls 140 kilowatts. A normal data center rack pulls 10. 8,000 kilometers of fiber inside one building, and the fastest links are still copper because light moves more slowly through glass than electrons through metal. Bookmark it tonight. Then read the article below.
zostaff235,592 просмотров • 24 дней назад
I BUILT A BOT THAT PREDICTS FOOTBALL MORE ACCURATELY THAN BOOKMAKERS 3 probability sources. ML model + Bet365 odds + Polymarket. When all three diverge - that's edge 5 seasons of EPL, La Liga, Bundesliga. 7,600+ matches. Each with goals, shots, possession, corners, cards, odds ELO rating using the FIFA formula - accounts for opponent strength, goal difference, home advantage. Not just W/D/L but the context behind every win xG proxy from basic stats - shots on target * 30% conversion + shots off target * 3%. Teams scoring more than they should - regression is coming Rolling averages over 5 matches, fatigue factor, head-to-head history, day of the week Claude API analyzes context the model can't see - motivation, pressure, derbies XGBoost + Random Forest + Logistic Regression in an ensemble. Walk-forward backtest, not random split Bookmaker says 55% home. Polymarket says 48%. Model says 52%. KL-divergence between sources = signal. The bigger the gap + the fatter the edge All three agree - I skip, zero edge. Two against one - I enter on the majority side Kelly sizes the position, Claude explains why
zostaff511,149 просмотров • 1 месяц назад
I MADE MY 3 CLAUDE BOTS COMPETE AGAINST EACH OTHER Same bankroll, different strategies, no overlap. Started at 7:17 PM, stopped 24 hours later. One fast, one patient, one reads patterns. MACD - 147 trades, hold time 8 min, sees volume spike and gets in before the crowd, win rate 61%, Sharpe 1.4, P&L: +$389 RSI + VWAP - 23 trades, hold time 4 hours, waited for extremum, RSI above 80 VWAP confirmed - entered against the crowd, win rate 74%, Sharpe 2.1, P&L: +$641 CVD - 31 trades, hold time 47 min, price goes up but money flows out - sells, price drops but money flows in - buys, win rate 58%, Sharpe 1.8, P&L: +$937 24 hours - +$1,967 Not a single trade overlapped, three bots one market three different answers. CVD won not because it's right more often but because when it's right it takes more. Correlation between them 0.12, when one loses the others don't lose with it. Diversification by logic.
zostaff636,850 просмотров • 2 месяцев назад
a sports analytics guy at MIT DM'd me after my last post "NBA stat models need 10 years of play-by-play data. you're telling me Claude does it from one weekend of prompts?" i told him Claude doesn't model games. it models markets. the Lakers don't need to be predicted. the Polymarket contract on the Lakers does. when the market is at 62% and my detector says 78%, that's the edge. i never touch the game itself. 3,000+ stars. open source framework. one prompt. 48 hours. 8 detectors running on every sports contract live. last 14 days across NBA playoffs: > 62 contracts entered > 49 winners > +$6,217 net he replied once: "so you arbitraged fan emotion" exactly. fan emotion is a price signal. the market underweights blowouts and overweights narrative. my bot only reads the first one. +$31,447 lifetime. 9 weeks. $1,600 seed. copytrade: he never DM'd again. but his team started a research project on "retail sentiment decay in sports contracts" three weeks later they publish. i'm nine weeks ahead of them.
zostaff399,899 просмотров • 1 месяц назад
🚨 HOW I BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES WITHOUT WRITING A SINGLE LINE OF CODE > One person built a trading bot in 34 minutes > Didn't write a single line of code manually > Just talked to Claude Code > The bot detects market regime on its own: bull, bear, sideways > Switches strategies for each regime automatically > 13 safety checks before every single trade > Stop-loss, circuit breakers, position limits, correlation check > Walk-forward backtest - not curve fitting, real simulation > Connected to Alpaca - free paper trading > 45 out of 45 tests passing > Full pipeline: data -> regime -> signal -> risk -> order -> log Hedge funds pay millions for this, He paid $0
zostaff358,962 просмотров • 2 месяцев назад
I SAW THIS ARTICLE AT 11:27 PM AND DIDN’T SLEEP UNTIL 4:11 AM Read it three times, then just... started building. Took the Avellaneda-Stoikov quoting logic. Wired it to the Hawkes process for order flow. Added the VPIN circuit breaker exactly like the article says. Ran 500 simulations tonight: > Mean P&L: +$312/session > Sharpe: 1.87 > Win rate: 68% > VPIN saves: 11 sessions > Max drawdown: -$890 The VPIN part is insane btw, 11 times it pulled my quotes before informed flow ran me over, without it sharpe goes negative, just like that. Kyle's lambda estimation is literally 30 lines of python, i had no excuse not to build this. Side effect, now i can't sleep.
zostaff436,082 просмотров • 2 месяцев назад
10 repos that mass replace a $100,000/year football analytics department. all free. all open source. -> replaces Hawkeye and Second Spectrum YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. calculates speed, distance, possession. from a TV feed. no sensors. -> replaces entire quant sports desk stacked ensemble: LightGBM + XGBoost + Neural Networks + Random Forest. scrapes FBRef automatically. ELO with dynamic K-factor. Poisson xG. MongoDB backend. the most complete open-source football prediction pipeline on GitHub. -> replaces paid prediction platforms ($30/mo) full GUI app. 7 ML algorithms. downloads data from football-data. co. uk. predicts upcoming fixtures. exports to Excel. one click. -> replaces manual feature engineering XGBoost with 354 hand-crafted features. works for any European league. data straight from football-data. co. uk. plug and predict. -> replaces value bet scanners ($50/mo) ELO + expected goals + offensive/defensive ratings. compares model probability vs Vegas lines. flags when you have edge. -> replaces bookmaker calibration tools Gradient Boosting tuned to output probabilities that match real bookmaker odds. not just accuracy - calibrated confidence. -> replaces StatsBomb xG subscription xG model from KU Leuven researchers. LogReg + XGBoost pipelines. supports Wyscout, StatsBomb, Opta data. academic grade. -> replaces xG analytics dashboards xG on StatsBomb open data. SHAP explanations for every prediction. proper calibration. tested on FIFA World Cup 2022. -> replaces basic prediction models Poisson distribution for goal simulation. the classical approach that still beats most ML models on draw prediction. -> replaces Premier League prediction services XGBoost + AdaBoost + SVM on EPL data. detailed EDA. confusion matrices. honest 56% accuracy - because football is hard. like + bookmark you'll need this when you build your first football prediction bot
zostaff220,425 просмотров • 1 месяц назад
Jim Simons agreed to do exactly one long-form interview about his life in math, codebreaking for the NSA, and building Renaissance Technologies. The Simons Foundation filmed it and dumped it on their website behind a Vimeo player nobody could find. Two hours and 40 minutes. Someone reuploaded it to YouTube on a channel with 483 subscribers. 749 people have watched it. The deepest Simons interview on the internet. And almost no one knows it exists. Bookmark it tonight. Then read the article below.
zostaff130,338 просмотров • 27 дней назад
AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.
zostaff229,505 просмотров • 1 месяц назад
Jim Simons made 66%/YEAR for thirty years at Renaissance and never let an outside dollar through the door. Andrew Lo runs the MIT Laboratory for Financial Engineering and trained half the senior quants at Citadel, AQR, and Two Sigma. In 2019 they sat down at MIT for one hour and 28 minutes. The only time Simons ever talked publicly about money with someone who understood the math. Sitting on the Finance at MIT channel. Almost nobody outside academia has watched it through. Bookmark it tonight. Then read the article below.
zostaff103,762 просмотров • 26 дней назад
a hedge fund emailed me at 11:23 AM in the morning. i just woke up, heard the email notification from the kitchen and went to check. thought it was something usual, a newsletter or github. i open it, a recruiter from a hedge fund. checked the domain was real, everything checks out. he read my article, went through my repo and wants to jump on a call. i never applied anywhere, never sent a cv, never networked, just built and published. ai-quant-researcher, open source, claude generates strategies and the system kills the bad ones. deflated sharpe as a hard gate, purged cv, leakage detector, kill switch, 111 tests. in the email he quoted a line from my own article back to me: the deflated sharpe as a hard gate caught my eye, that's not something we see often from outside the industry. a year ago i was reading lópez de prado on weekends and writing python in the evenings, just because it was interesting. nobody paid me, nobody asked, nobody knew i was doing this. today there's an email in my inbox from one of the largest hedge funds in the world. this is how it works.
zostaff108,387 просмотров • 28 дней назад
An ex-Jane Street trader revealed, How Sam Bankman-Fried behaved on day one of his internship. Four years before FTX. Why Jane Street makes millions of tiny trades instead of one big one. What they did in 60 seconds when their model broke in 2008. Why the offer isn't about math. Almost nobody has seen this interview. Bookmark it tonight. Then read the article below.
zostaff54,401 просмотров • 17 дней назад