Livsun
@L1vsun • 4,143 subscribers
US Entrepreneur × Quant Trading × Finance Tech For collab & business - tg: @l1vsun
Shorts
Videos
the finance kids everyone envied in college made $120k at goldman the quiet math kids nobody noticed made $650k at firms most people can't name jane street. citadel. two sigma. d.e. shaw they don't recruit from linkedin. they recruit from math olympiads and competitive programming leaderboards the filter is 4 things: probability, coding, mental math, game theory same problems recycled every year, same structure every round the prep path is documented, free, and takes 8 months a kid from a state school who grinds this for 8 months has beaten ivy leaguers who showed up unprepared - credential matters less than the pattern recognition most people spend 3 years trying to break into banking for $120k the people who looked one level up spent 8 months and landed 5x the salary the information to do this has existed for years Bookmark this and upgrade your brain the gap isn't talent it's that nobody told you where to look
Livsun1,284,610 次观看 • 4 个月前
claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had
Livsun110,496 次观看 • 24 天前
a physics phd accidentally found how Citadel knows when to enter 18 minutes before price moves he was studying particle drift equations. published it on arxiv in 2019. nobody in trading ever noticed the math mapped perfectly onto order book dynamics here's the part that should bother you: retail tracks price. price is the last signal in the chain, not the first institutional money hits options markets before equity - always - because a $400M equity entry creates slippage they can't mask, but the options hedge is harder to detect that hedge shows up in one place: 25-delta put skew, live, compared against its 20-day rolling baseline when skew compresses 1.3+ standard deviations below baseline on SPY, a major buyer is positioning signal precedes price by 6 to 18 minutes on average cboe publishes this data for free, in real time, every single trading day ran it on 14 months of live signals > 71% win rate > avg hold: 26 minutes > positive expected value every month, no exceptions math to calculate it is 40 lines of python data costs nothing paper explaining the physics analog has been on arxiv since 2019 nobody linked it in your trading group because your trading group learned RSI from a youtube channel that also sells a $297 course Bookmark this they kept you debating indicator settings and timeframes while the actual institutional footprint was sitting in public options data, completely free, the entire time
Livsun111,149 次观看 • 26 天前
a citadel execution trader posted his resignation letter two years ago buried in paragraph three: "the retail positioning data we received was the single most reliable signal in our entire book" nobody talked about it. i couldn't stop thinking about it every time you place a market order on robinhood or webull, it hits a wholesaler before any exchange ever sees it. citadel pays $3 billion a year for that privilege - to know exactly where retail is leaning before price moves that data is technically public. the SEC mandates quarterly PFOF disclosures on EDGAR, every wholesaler, every quarter so i built a model off it: when retail is net long >68% on SPY options, market makers are holding the offsetting short gamma - and market makers don't speculate, they hedge immediately, mechanically, in the same direction every single time that hedge pressure moves price against retail positioning in 71% of cases on a 3-5 day window only question worth asking isn't "where will the market go?" - it's where is retail positioned right now, and where does the mechanical hedge force price as a direct consequence data is free, EDGAR has all of it, weekend of python to build Bookmark this they've been showing you the wrong scoreboard your entire trading life. the one that matters is the one where the people who bought your order flow are keeping score
Livsun69,173 次观看 • 21 天前
a russian mathematician solved trading in 1906 wall street found the paper 70 years later, built billion-dollar funds around it, never mentioned it to retail andrei markov proved markets aren't random - they're state machines trending, ranging, reversing - each state has a fixed historical probability of shifting to the next build a transition matrix from real data: trending -> stays trending: 68% trending -> flips to range: 21% trending -> reverses: 11% now you're not predicting direction. you're entering on 68% historical completion identify your state, size with kelly, take the trade when math says yes that's the edge. the whole edge renaissance has run this since 1988. 37 years of 66% annual returns paper is free, data is free, implementation is 200 lines of python Bookmark and use in your own strategy they kept you staring at candles while they ran probability tables
Livsun376,877 次观看 • 3 个月前
a hotel front desk clerk in nashville figured out why markets move exactly when they do not direction, not news - the actual mechanism of why a move happens at all he works overnight shift, 11pm to 7am. lobby goes quiet after midnight, nothing but a monitor and a wifi connection question that started it: why does volatility cluster he'd read it in passing - options dealers cause price moves they didn't intend spent 6 hours across two nights searching, wrote everything into a google doc called "options thing" here's what he found when you buy a call option from a dealer, dealer has a new problem. they sold you the right to buy shares at a certain price if stock moves up, your option gains value and dealer owes you money. to protect themselves they have to buy shares immediately - no discretion, no delay amount they have to buy at every price level is published every second for free - it's open interest on the options chain. every brokerage shows it he built a spreadsheet every morning at 9:29am, one minute before open, he pulled SPY's options chain and calculated where dealers were most exposed marked strikes with heaviest call open interest. watched what happened in first 30 minutes of trading day 12 he stopped breathing for a second price moved to the strike with heaviest dealer exposure 73% of the time in the first 45 minutes not because of a chart pattern, not because of any signal because 400 dealers ran the same hedge calculation at open, and all of them had to buy the same shares at the same time he started calling it gravity price pulls toward certain strikes when dealer positioning is heavy enough - not prediction, mechanics math has a name: gamma exposure, or GEX SpotGamma built a whole company surfacing it. Squeeze Metrics published an academic paper on mechanics in 2018 python implementation is around 400 lines, nothing but the options chain you already have he built it in google colab over 3 weekends, free, working only on nights the lobby was empty tracked it against 60 days of live SPY data on negative GEX days - dealers short gamma, forced to amplify moves - average daily range expanded 2.8x on positive GEX days, 63% of sessions closed within half a percent of open this is not a signal. it's a regime classifier negative GEX: something moves big today, whichever direction gets started. buy straddles, size up, let dealers carry it positive GEX: nothing moves today. dealers kill every attempt before it gets 2 points sell premium, collect theta, sleep at month 4 he went live. $4,200 account, pure options, no directional bet six months later: $4,200 became $19,800 he still works overnight shift. told me about it in the lobby at 3am when i asked what he was typing google doc still says "options thing" - he never renamed it i asked why he never shared this. he looked at the lobby doors and said "who would believe a hotel clerk" data is free, formula is public, wall street has run this since 2017 they assumed retail would never think to read options flow as a mechanical map of where price has to go they were right about retail. they weren't right about him bookmark this and go build it market tells you exactly where it's going. you just have to stop reading the wrong layer Write your thought below
Livsun202,091 次观看 • 2 个月前
a citadel quant told me something that broke my entire trading framework "we don't predict markets. we model the state machine" he explained markov chains in 90 seconds the market is never random - it always exists in one of three states trending up, trending down, ranging - each has a fixed probability of shifting to another build the transition matrix from real price data: > trending up -> 68% stays trending, 21% flips to range, 11% reverses > ranging -> 54% stays range, 28% breaks up, 18% breaks down > trending down -> 61% stays falling, 24% flips to range, 15% reverses now you're not guessing, you're playing probability identify current state, enter with the 68% edge, size with kelly criterion based on that probability the formula is public - markov published it in 1906 hedge funds use it, the math costs nothing what costs you is asking the wrong question "where is price going?" is random "what state am I in right now?" has an answer transition matrix built from 10 years of data is your edge Bookmark it not a signal, not an indicator - just conditional probability that compounds every single trade
Livsun269,433 次观看 • 4 个月前
83% of options expire worthless wall street has known this since 1973, it's literally in the original black-scholes derivation they just never told retail which side of that number to stand on implied volatility - what options cost - chronically overstates what volatility actually delivers. not sometimes. structurally. for 40 years straight the gap has a name: volatility risk premium IV runs at 22%. actual realized vol delivers 14%. you pocketed 8 vol points without predicting a single direction, a single earnings move, a single fed decision quant vol desks have been harvesting this gap the whole time: > pull 30-day realized vol from price history (free on any data terminal) > compare to current IV on the options chain (visible on any broker platform) > enter when the spread sits 1.5 standard deviations above its own historical mean > kelly-size based on the 83% historical close rate the spread always closes. vol reverts. that's not a prediction - it's what 40 years of options data shows without a single exception insurance companies don't predict which house burns. they write enough policies that 83% probability compounds into something inevitable every single quarter quant desks run the exact same logic on options markets retail keeps buying calls hoping to be directionally right once. the firm on the other side doesn't care which way price goes the data to build this yourself has been public since 1973. the math is 4 lines of python. every derivatives textbook covers it in chapter 2 Bookmark this they've been on the other side of your trades the whole time
Livsun81,605 次观看 • 1 个月前
you're not bad at trading. the game is just designed so you never find out what the actual game is retail gets candlestick patterns and RSI. the kids at citadel get probability theory, transition matrices, and kelly criterion from day one they literally filter recruits who don't already know this. they don't teach it to you, they find people who found it themselves the math that runs renaissance technologies has been sitting in free statistics textbooks since the 1950s markov chains, autocorrelation, state detection - none of it is secret, none of it is locked behind a paywall the information gap isn't about money or access it's about the fact that the finance industry makes more selling you indicators and courses than it ever loses to you trading "retail investor" is industry language for "someone we extract from" meanwhile a 24 year old with a stats degree and 6 months of python experience is clearing $400k at a prop desk same markets. completely different question being asked you ask: is it going up or down they ask: what state is the market in and what does history say happens next one of those questions has an answer. the other one is just noise dressed up as analysis the textbook is free. the math is learnable. the only thing that wasn't free was someone telling you it existed
Livsun22,621 次观看 • 10 天前
a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
Livsun189,343 次观看 • 4 个月前
retail has 10,000 indicators and still loses 80% of the time quants have one number they pull every morning before touching anything else it's called options skew - the gap between how much downside protection costs versus upside exposure on any liquid name CBOE publishes it free, every 15 minutes, on every major ticker since 2004 when put vol runs 8+ points above call vol on SPY, institutional desks are paying premium to hedge hard - that's not noise, that's where money thinks risk actually is backtested 2009 to 2024: when skew spikes 1.5 sigma above its 90-day baseline, SPY underperforms next 15 sessions 68% of the time not predicting direction - reading where billion-dollar desks are quietly paying to protect themselves size with kelly at 68% edge, enter short, exit in three weeks retail spent years memorizing moving average crossovers quants spent the same years reading a free number nobody told them existed same market. completely different question bookmark this data was public the whole time, you just didn't know to look for it
Livsun79,282 次观看 • 1 个月前
a risk analyst at two sigma told me something over drinks i still think about "our models are right 54% of the time. retail models can be right 60% and still blow up" i asked him why "you trade every day. we don't" two sigma sits completely flat roughly 40% of the year - not because signals are missing, because regime filter says math doesn't work in that environment trending regime eats mean reversion alive - ranging regime eats momentum most retail accounts blow up not from bad signals but from running the right signal in the wrong regime framework is called regime detection - published academic work, in every quant textbook since the 1990s 3 inputs to identify it: realized vol vs 30-day mean, autocorrelation of daily returns, volume profile vs 20-day average vol elevated + autocorrelation negative = mean-reversion regime - momentum will destroy you here autocorrelation positive + vol compressing = trending regime - reversion will destroy you here wrong regime turns a 60% signal into a 40% signal - you're paying the house right regime turns a 54% signal into a 65% edge - kelly compounds it into something insane bookmark this before you open another chart Dm me "Quant" for full quant course data is free, formula is public, 150 lines of python to run the whole thing they didn't hide the signals from you they hid the question you were supposed to ask first
Livsun120,924 次观看 • 2 个月前
a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free
Livsun61,938 次观看 • 1 个月前
a quant at Two Sigma told me something at a bar i can't stop thinking about "retail looks at price. we look at the autocorrelation of price changes - completely different signal" i asked him to explain it like i was 12 he drew on a napkin: if today's move predicts tomorrow's move - even slightly, 53% of the time - that's an edge that edge, sized with Kelly, compounds into something insane data is free - Bloomberg and the Fed publish all of it. math takes a weekend to learn reason retail loses isn't intelligence. it's that they're reading the wrong representation of the same data "a chart hides serial correlation. a time series shows it naked" went home, ran autocorrelation tests on 3 years of SPY data found 4 patterns - statistically significant, all exploitable on a 5-day window signals aren't perfect, right 58% of the time. Kelly says that's enough data was free the whole time, framework sitting in every stats textbook. nobody pointed retail toward it they kept you staring at candles
Livsun150,025 次观看 • 4 个月前
the fed publishes data every single week that D.E. Shaw and Two Sigma actively trade on retail has no idea it exists. it's free. has been for decades it's called the H.4.1 - the federal reserve's own balance sheet release, every thursday at 4:30pm when bank reserves expand above their 12-week rolling mean, equity volatility compresses in a statistically predictable window 71% historical consistency since 2009 quants call this liquidity regime detection. the framework is in any macro textbook, chapter 4 not a chart pattern. not an indicator. a state the market is already in before you open a single candle high reserves -> collateral abundance -> risk appetite expands -> vol falls -> entry window opens you identify the state from free weekly data, enter when the math says the state historically sustains, exit when reserve growth rate rolls below the 8-week threshold avg hold: 6 to 11 days win rate: 71% kelly sizing turns that into compounding edge, not luck the data source: fred.stlouisfed. org, free API, zero subscription the framework: standard monetary transmission mechanics, any macro textbook the implementation: 150 lines of python Bookmark this before you scroll past it they told you to stare at RSI while they were reading the fed balance sheet every thursday at 4:30
Livsun31,696 次观看 • 25 天前
every day before market open, the CBOE publishes exactly where market makers are forced to buy and sell not a theory, not a pattern - a number that actually moves price it's called dealer gamma exposure - GEX - and it creates real price magnets quants have traded against for years here's why it works when you buy a call option, a market maker sells it to you and has to hedge that hedge shifts as price moves - they buy when it rises, sell when it falls negative GEX zones are where they sell into every rally, cap every breakout, crush every momentum move strikes with highest open interest aren't random - they're gravitational markets don't break through those levels cleanly because a billion-dollar dealer is actively hedging against it quants call them gamma walls data is free, published every morning on the CBOE site - no API, no Bloomberg terminal retail calls those same levels "resistance" and chalks it up to vibes bookmark this before it lost it's not vibes. it's math someone is paid $400k/year to understand while you do it by eyeball the whole time you thought technical analysis was detecting patterns you were just watching the shadow of dealer hedging and calling it insight
Livsun71,167 次观看 • 3 个月前
a physics dropout from ohio has a $2.4m trading account he never read a trading book. never learned technical analysis. just took equations from his thermodynamics textbook and pointed them at price data to see if they fit they did markets under stress behave almost identically to thermodynamic systems. energy dissipates in predictable patterns after compression. price does the exact same thing the concept is entropy. shannon formalized it in 1948. it's in every stats and physics textbook that's ever been printed his actual setup: measure entropy across the last 40 candles flag low-entropy compression states build a transition table from 3 years of real data what the table showed: 68% of low-entropy compression states resolve into directional expansion above 2 ATR within 5 candles that's not a prediction. it's a probability table the market keeps filling in for you, every single day, for free he sizes each trade with kelly based on the 68%. takes it when math says yes, skips it when math says no traders in his server spent 3 years learning to read candles better he spent 6 months just asking a question they never thought to ask the textbook was $40. the data is free. the math has existed since 1948 bookmark this nobody told you about it because the second you start asking better questions, you stop being the other side of their trade
Livsun31,722 次观看 • 1 个月前
market opens at 9:30am his trade was already in at 4am neural net trained on 11 years of tick data called that setup 5 hours before candle even formed he's not smarter than the market. just built something that reads patterns human eyes can't process fast enough 4,200 data points per second, 847,000 labeled historical setups, running on a $40/mo server surfaces 3-4 trades a day. he takes top 2 last 90 days: 71% win rate, 2.3 avg risk/reward while retail traders watch news at open, this system already decided before sunrise you're not losing because your analysis is wrong you're losing because you're competing with something that doesn't sleep, panic or second-guess itself most people think this requires a PhD and a $2M quant desk he built his whole setup for under $500 using a free dataset and 3 weeks of evenings Bookmark this setup edge was never hidden behind a paywall or locked inside a fund it was sitting in a format nobody bothered to train on
Livsun70,370 次观看 • 3 个月前
jane street, two sigma, man group put their actual code on github 22 repos from firms running $200 billion combined - all public, all free nobody's talking about this because nobody thought to check what's in those repos isn't just tooling - it's their mental model these firms don't ask "will price go up?" they model markets as adversarial games - every participant simultaneously optimizing against every other that's why their signals hold - retail's don't two sigma's repo covers how they structure and clean data at scale man group's work spans signal generation, portfolio construction, factor models jane street's tools teach probability and microstructure exactly how their quants think this is game theory applied to markets - nash equilibria, auction dynamics, opponent modeling not chart patterns or indicator crossovers code is free, frameworks in public repos, math in every probability textbook Bookmark before it gets buried retail paid for courses on same stuff these firms gave away information gap was never intelligence, just knowing where to look
Livsun61,594 次观看 • 3 个月前
a flood scientist accidentally solved stock market prediction in 1951 he wasn't studying markets. he was modeling nile river overflow patterns for the egyptian government harold hurst discovered something that broke the assumptions behind modern finance: natural systems have "memory" - the recent past predicts the near future at a statistically measurable level quants found his paper buried in hydrology journals 40 years later, built regime-detection desks around it, kept it off retail radar the number is called the Hurst Exponent. single value, 0 to 1: above 0.5 = market has momentum, recent moves predict continuation below 0.5 = market is mean-reverting, extremes snap back to the average at 0.5 = pure random walk, no edge exists SPY's 3-year hurst on daily closes: 0.61 not random. statistically confirmed to trend. a flood hydrologist proved it before most traders alive were born when H > 0.58: trend-following has positive expected value - bet with momentum, hold longer when H < 0.42: mean reversion has the edge - fade the extremes, take fast profits you're not predicting direction. you're detecting the regime first, then picking the right strategy for it this is the actual reason 90% of traders lose - not bad entries, not bad exits, not bad indicators - just using a trend strategy in a mean-reverting market and a mean-reversion strategy in a trending one wrong tool. wrong regime. every time formula is in any time series textbook. raw data is free on yahoo finance. implementation is 40 lines of python Bookmark this before you forget they built billion-dollar regime-detection desks on a nile river scientist's math while you were drawing support lines on the wrong type of market entirely
Livsun15,492 次观看 • 23 天前