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Taleb: "standard deviation is not how much something moves on average." ask anyone - even statisticians, even government agencies - and that's exactly the wrong definition you'll get back. what they're describing is mean absolute deviation. real standard deviation squares the moves first - which quietly hands almost all... show more

0xDipper

6,788 subscribers

414,018 просмотров • 3 месяцев назад •via X (Twitter)

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Комментарии: 19

Фото профиля Enrique Meza Costeno

Enrique Meza Costeno3 месяцев назад

The deeper philosophical problem is this: the standard deviation is the dispersion metric in almost all classical statistics precisely because it makes algebra tractable (squaring makes derivatives clean, distributions conjugate, etc.). It was chosen for mathematical convenience

Фото профиля JiveSeattleTurkey 🔱 🫎 🔱 🫎 🔱 🫎

JiveSeattleTurkey 🔱 🫎 🔱 🫎 🔱 🫎3 месяцев назад

Hey @nntaleb - has anyone bothered to prove/disprove or revise a single textbook? Or are they so hidebound they can’t?

Фото профиля Jay Vijay

Jay Vijay3 месяцев назад

@grok that standard deviation calculation in talebs example seems wrong. Can you check?

Фото профиля charles halaby

charles halaby3 месяцев назад

This is thoroughly embarrassing.

Фото профиля F

F3 месяцев назад

@grok show with simple numbers what professor taleb is teaching

Фото профиля 𝑨𝒔𝒉𝒊

𝑨𝒔𝒉𝒊3 месяцев назад

Standard deviation isn’t “average movement.” It’s how bad the rare events can hit you. Most people still don’t get it.

Фото профиля Juan

Juan3 месяцев назад

Standard deviation = how well does the average represent the whole population

Фото профиля Clodex

Clodex3 месяцев назад

aleb banger is here

Фото профиля Joe the average

Joe the average3 месяцев назад

As far as I remember Taleb has a record of correct prediction precisly zero. It's easy to estimate events afterwards. It is called economics.

Фото профиля Dipanshu Kushwaha

Dipanshu Kushwaha3 месяцев назад

That's a solid point! It's wild how often definitions get mixed up. Precision matters, especially in stats. Glad you’re shedding light on this!

Фото профиля Agus Peinado

Agus Peinado3 месяцев назад

I don’t get what he is pointing out. Could you explain it again, please?

Фото профиля Ninedol

Ninedol3 месяцев назад

he is a true legend, i will save it

Фото профиля Severian

Severian3 месяцев назад

conditional tail expectation is where it's at

Фото профиля RandomMax

RandomMax3 месяцев назад

Thanks for sharing all these clips and lectures especially from Mr Taleb. Very interesting.

Фото профиля Roli

Roli3 месяцев назад

Very useful reminder. Difference between thinking what we know, and what we really know and then how we apply. Signal loss all over 😔

Фото профиля Marc Vetter

Marc Vetter3 месяцев назад

Love it, reminds me of the most interesting classes and lectures

Фото профиля Nazym Azimbayev

Nazym Azimbayev3 месяцев назад

Obvious things explained. it seems that @nntaleb just became old

Фото профиля Emilio Andres German🇦🇷🇮🇹

Emilio Andres German🇦🇷🇮🇹3 месяцев назад

Messi

Фото профиля Crypto Mavka

Crypto Mavka3 месяцев назад

taleb is dropping truth bombs as always. standardizing risks through mean deviation is really a trap for many.

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Joseph Noel Walker

578,223 просмотров • 2 лет назад

quant funds spend millions hiring people who know a formula you learned in 10th grade and never applied to a price chart y = mx + b. that's it linear regression - same math from high school most traders learned, ignored, and forgot existed what quants actually figured out: asset prices don't move randomly, they drift and revert with measurable slope regression separates signal from noise. slope tracks trend, residuals measure the deviation when deviation gets large enough, that's the trade - prices tend to snap back to the line quants call it "mean reversion." retail calls it "a dip" actual workflow: > pull 5 years of daily returns > run OLS, extract beta and residuals > z-score the residuals > entries at -2 standard deviations, exits at 0 retail draws trend lines by hand and calls it analysis Bookmark this ^ data is public, math is free, approach has been in papers since the 1980s 99% of traders have never built one signal this way it was never an information gap - always been a methodology gap, and that's the kind that compounds

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Livsun

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Nassim Taleb asked a room full of quants: to trust an average, a normal bell curve needs ~30 data points. how many does an 80/20 "fat-tailed" world need? people guessed 200. 1,000. the real answer: 100 trillion. that's 10¹⁴ - because in fat-tailed data, one rare event dwarfs everything else, and until it shows up your sample is a lie. "you observe, you observe, you observe… until the crash." worse, the deception only runs one way. a wild distribution can disguise itself as calm - a calm one can't fake being wild. so the data almost always fools you downward, into thinking there's less risk than there is. the fix: stop estimating the average from the average. it doesn't work where it matters. ~25-min talk, free. Taleb on why your data is quietly lying about risk ↓

0xDipper

204,184 просмотров • 3 месяцев назад

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TBPN

57,070 просмотров • 4 месяцев назад

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Tom Yeh

17,011 просмотров • 2 месяцев назад

I gave Claude historical weather API vs Polymarket discrepancies and said: "find the gap where the forecast shifts but the price lags" it built the Weather Arb Terminal this isn’t predicting the rain it’s exploiting the latency between a NOAA satellite update and a retail trader’s reaction the screen shows engine in full execution: > 247 active contracts scanned > 86.8% accuracy on deviation spikes > +$5,475.80 in a single session > 462 trades. Zero opinions. the logic is a 4-stage execution pipe: scan → discrepancy → filter → execute 18:59:17 - NYC Frost forecast shifts from 41% to 58% on NOAA/AccuWeather - market is asleep at $0.44. 17-point edge - the bot hits the bid before the headline even hits the feeds - 19:02:14 - Market catches up. Price spikes to $0.61 +$534.20 in 3 minutes it doesn't care if it's snowing or sunny It only cares that the probability changed and the price didn't while the crowd debates climate change, the network reads standard deviations > fire rate: 41 Hz > latency: 3ms > edge: 16pt avg the network doesn’t get tired. it doesn’t hope. it waits for the math to break, then it collects. copy the engine here: the forecast is noise. the deviation is the signal

I gave Claude historical weather API vs Polymarket discrepancies and said: "find the gap where the forecast shifts but the price lags" it built the Weather Arb Terminal this isn’t predicting the rain it’s exploiting the latency between a NOAA satellite update and a retail trader’s reaction the screen shows engine in full execution: > 247 active contracts scanned > 86.8% accuracy on deviation spikes > +$5,475.80 in a single session > 462 trades. Zero opinions. the logic is a 4-stage execution pipe: scan → discrepancy → filter → execute 18:59:17 - NYC Frost forecast shifts from 41% to 58% on NOAA/AccuWeather - market is asleep at $0.44. 17-point edge - the bot hits the bid before the headline even hits the feeds - 19:02:14 - Market catches up. Price spikes to $0.61 +$534.20 in 3 minutes it doesn't care if it's snowing or sunny It only cares that the probability changed and the price didn't while the crowd debates climate change, the network reads standard deviations > fire rate: 41 Hz > latency: 3ms > edge: 16pt avg the network doesn’t get tired. it doesn’t hope. it waits for the math to break, then it collects. copy the engine here: the forecast is noise. the deviation is the signal

Hanako

39,093 просмотров • 6 месяцев назад

implied volatility is a price. realized volatility is what actually happens. the gap between them is one of the most durable edges in markets the chart in that clip is the whole story each dot is a day: what options priced in (IV) versus what the market actually delivered over the next 30 days (RV) the regression line has a negative slope and sits below the diagonal that isn't noise. it's the variance risk premium VRP = IV − RV on average, across decades, implied vol prints higher than the volatility that follows options are, structurally, priced for more movement than actually shows up the reason isn't a mistake. it's insurance whoever sells an option is underwriting risk, the same way an insurer underwrites a house fire they demand a premium above fair value to carry that risk, and buyers pay it because they want protection so the seller of volatility is the insurance company and insurance companies, on average, win. not every policy. on average, over a large book that's the edge retail never sees because retail is almost always the buyer buying calls, buying puts, paying the premium, standing on the losing side of a spread that is baked into the price before the trade even opens on the clip's data: IV was 10.95%, the realized vol that followed was higher on that specific day that's the risk. VRP is positive on average, not always. sometimes RV blows past IV and the seller takes the loss which is exactly why it's a premium and not free money you get paid to hold a risk that occasionally hurts. the edge is that the payment, over enough independent bets, exceeds the damage a desk harvests this systematically: sell the overpriced vol, hedge the direction, collect the spread, size so no single blowup ends the book retail buys the lottery ticket on the other side and wonders why theta bleeds it dry the data is public. IV comes off the options chain, RV is just the standard deviation of returns compute both, subtract, and the premium is right there on a chart you can build in an afternoon the number was never hidden retail was just standing on the paying side of it the whole time full breakdown in the article below

implied volatility is a price. realized volatility is what actually happens. the gap between them is one of the most durable edges in markets the chart in that clip is the whole story each dot is a day: what options priced in (IV) versus what the market actually delivered over the next 30 days (RV) the regression line has a negative slope and sits below the diagonal that isn't noise. it's the variance risk premium VRP = IV − RV on average, across decades, implied vol prints higher than the volatility that follows options are, structurally, priced for more movement than actually shows up the reason isn't a mistake. it's insurance whoever sells an option is underwriting risk, the same way an insurer underwrites a house fire they demand a premium above fair value to carry that risk, and buyers pay it because they want protection so the seller of volatility is the insurance company and insurance companies, on average, win. not every policy. on average, over a large book that's the edge retail never sees because retail is almost always the buyer buying calls, buying puts, paying the premium, standing on the losing side of a spread that is baked into the price before the trade even opens on the clip's data: IV was 10.95%, the realized vol that followed was higher on that specific day that's the risk. VRP is positive on average, not always. sometimes RV blows past IV and the seller takes the loss which is exactly why it's a premium and not free money you get paid to hold a risk that occasionally hurts. the edge is that the payment, over enough independent bets, exceeds the damage a desk harvests this systematically: sell the overpriced vol, hedge the direction, collect the spread, size so no single blowup ends the book retail buys the lottery ticket on the other side and wonders why theta bleeds it dry the data is public. IV comes off the options chain, RV is just the standard deviation of returns compute both, subtract, and the premium is right there on a chart you can build in an afternoon the number was never hidden retail was just standing on the paying side of it the whole time full breakdown in the article below

delost

23,951 просмотров • 2 месяцев назад

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. We have funded people who had a great idea, perfect background on paper, a great product, and still failed—and it has usually been that they’re insufficiently determined.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. The degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.”

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. We have funded people who had a great idea, perfect background on paper, a great product, and still failed—and it has usually been that they’re insufficiently determined.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. The degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.”

Startup Archive

32,513 просмотров • 2 лет назад

Palantir co-founder Joe Lonsdale on what actually separates Peter Thiel, Alex Karp and Elon Musk from everyone else - how they think, how they pick people, and why it keeps working on Musk: "Elon is the most alpha of all of my friends - I'm willing to go three or four standard deviations too hard in terms of working too hard and pushing people and being bold - Elon's my only friend who's clearly a standard deviation further than me" "Peter will find the view that others are missing and put a lot of weight behind it - he'll identify the one factor that matters that people aren't thinking about" on Karp: "there's a weird form of wisdom where he'll interact with you and know tons of things about you, and know if he wants to hire you or not - it's like a magic trick" "the guy who started Cognition won the gold medal in programming three years in a row - then hired 20 other kids who were the top gold and silver medalists of these Olympiads - it's the fastest growing company I've ever been in" bookmark & watch the full conversation ↓

Palantir co-founder Joe Lonsdale on what actually separates Peter Thiel, Alex Karp and Elon Musk from everyone else - how they think, how they pick people, and why it keeps working on Musk: "Elon is the most alpha of all of my friends - I'm willing to go three or four standard deviations too hard in terms of working too hard and pushing people and being bold - Elon's my only friend who's clearly a standard deviation further than me" "Peter will find the view that others are missing and put a lot of weight behind it - he'll identify the one factor that matters that people aren't thinking about" on Karp: "there's a weird form of wisdom where he'll interact with you and know tons of things about you, and know if he wants to hire you or not - it's like a magic trick" "the guy who started Cognition won the gold medal in programming three years in a row - then hired 20 other kids who were the top gold and silver medalists of these Olympiads - it's the fastest growing company I've ever been in" bookmark & watch the full conversation ↓

Сarm1ne

206,187 просмотров • 2 месяцев назад

Right now, the agitators think Trump is about to back down because of "poll numbers" according to Nick Sortor and Cam Higby 🇺🇸 who are on the ground in Minneapolis: "You have to make an example out of this place ... right now they firmly believe they're going to get away with all of this, and that ICE is going to back down. "You know, they're talking about these poll numbers, which I don't even believe are real poll numbers. Because if you talk to actual people on the street, they don't want to live next to Venezuelan rapists." This is a critical test of the administration's will. We must have ZERO tolerance for lawlessness. Squash the agitators quickly, the rest will get the memo, and the news cycle moves on.

Right now, the agitators think Trump is about to back down because of "poll numbers" according to Nick Sortor and Cam Higby 🇺🇸 who are on the ground in Minneapolis: "You have to make an example out of this place ... right now they firmly believe they're going to get away with all of this, and that ICE is going to back down. "You know, they're talking about these poll numbers, which I don't even believe are real poll numbers. Because if you talk to actual people on the street, they don't want to live next to Venezuelan rapists." This is a critical test of the administration's will. We must have ZERO tolerance for lawlessness. Squash the agitators quickly, the rest will get the memo, and the news cycle moves on.

Andrew Kolvet

79,838 просмотров • 8 месяцев назад

WOW: Arizona is on the verge of passing a Constitutional Amendment to prohibit medical mandates. “Not just for vaccines.” “For all medical choices.” “It would be the first and only state that has that.” And it has the full support of gubernatorial candidate Rep. Andy Biggs. It already passed the State House. If it passes the State Senate, it will become a referendum on the 2026 ballot. Which means Governor Katie Hobbs could not veto it. She just vetoed the Arizona Medical Freedom Act, which would have banned almost all medical mandates statewide. So health freedom supporters moved to bypass her veto with a Constitutional Amendment. Rep. Biggs: “Can you imagine what that would mean throughout the entire country?” “It would really set the standard.” “I really want to get Arizona back to being the standard economically and on freedom issues.” “We just need a little catalyst.” Rep Andy Biggs Aaron Siri

WOW: Arizona is on the verge of passing a Constitutional Amendment to prohibit medical mandates. “Not just for vaccines.” “For all medical choices.” “It would be the first and only state that has that.” And it has the full support of gubernatorial candidate Rep. Andy Biggs. It already passed the State House. If it passes the State Senate, it will become a referendum on the 2026 ballot. Which means Governor Katie Hobbs could not veto it. She just vetoed the Arizona Medical Freedom Act, which would have banned almost all medical mandates statewide. So health freedom supporters moved to bypass her veto with a Constitutional Amendment. Rep. Biggs: “Can you imagine what that would mean throughout the entire country?” “It would really set the standard.” “I really want to get Arizona back to being the standard economically and on freedom issues.” “We just need a little catalyst.” Rep Andy Biggs Aaron Siri

Health Freedom Defense Fund

38,850 просмотров • 2 месяцев назад

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Big Brain AI

366,743 просмотров • 6 месяцев назад

Dwyane Wade says he will have a standard for the Miami Heat if they like it or not and calls out Heat reporter that asked Bam the buzz question😳 “It’s standards. If the Miami don’t want me to have standards on the players that’s on them…I will always have a standard for the organization…because I gave so much to the organization. I have a lot less millions in my account, I have a lot less stats on the board…my standard isn’t play-in…that may be someone’s standard…the thing I don’t like about the question that Anthony — first all wrong timing to bring that up…you don’t talk about a f*ckin former player saying the buzz in the city is not there — you’re looking for something…I will address with him like this…do your job…like I called Bam when he was struggling this year and I gave him that bold of confidence to get his as* back on track…that’s who we are…it nothing personal…” (via Wy Network by Dwyane Wade/DWade)

Dwyane Wade says he will have a standard for the Miami Heat if they like it or not and calls out Heat reporter that asked Bam the buzz question😳 “It’s standards. If the Miami don’t want me to have standards on the players that’s on them…I will always have a standard for the organization…because I gave so much to the organization. I have a lot less millions in my account, I have a lot less stats on the board…my standard isn’t play-in…that may be someone’s standard…the thing I don’t like about the question that Anthony — first all wrong timing to bring that up…you don’t talk about a fckin former player saying the buzz in the city is not there — you’re looking for something…I will address with him like this…do your job…like I called Bam when he was struggling this year and I gave him that bold of confidence to get his as back on track…that’s who we are…it nothing personal…” (via Wy Network by Dwyane Wade/DWade)

The HEAT Realm

904,454 просмотров • 7 месяцев назад

$same crash, same window: this strategy ended at $117, buy-and-hold at $67 the difference is one operation in the formula on screen M_t = ( Σ r_{t-i} ) / ( σ_t · √N ) the top is momentum: just the sum of recent returns. net directional drift the bottom is the part retail never adds: divide by volatility that denominator is the whole edge raw momentum has a fatal flaw. a 2% move in a calm market and a 2% move in a panic look identical to it but they are not the same signal. one is information, the other is noise wearing a big number dividing by σ_t rescales every signal into the same risk units now a move only counts as momentum if it's large relative to how much the asset is currently shaking strong drift in a quiet tape scores high. the same drift inside chaos scores near zero this is why the strategy survived the drawdown that ate buy-and-hold when volatility exploded, the denominator exploded with it, the signal shrank toward zero, and the position sized itself down automatically no rule that said "reduce risk in a crash." the math did it because σ_t was in the denominator this is called time-series momentum, and it's one of the most documented effects in finance moskowitz, ooi and pedersen, AQR, 2012: it worked across 58 markets, every asset class, back to 1900 the reason it keeps working is structural, not a pattern trends persist because information diffuses slowly and institutions can't enter all at once. a pension fund moving billions takes weeks, and that slow entry is the drift the signal captures retail buys the move and gets bigger as it accelerates, which means biggest right before the reversal a desk scales inversely to volatility, which means it's largest when the trend is clean and smallest when it's about to snap the paper is free. the whole thing is a rolling sum divided by a rolling standard deviation ten lines of python, twenty years of data that never cost anything the momentum was never the edge. everyone can see a trend the edge was dividing it by the one number that tells you whether to believe it full breakdown in the article below$

same crash, same window: this strategy ended at $117, buy-and-hold at $67 the difference is one operation in the formula on screen M_t = ( Σ r_{t-i} ) / ( σ_t · √N ) the top is momentum: just the sum of recent returns. net directional drift the bottom is the part retail never adds: divide by volatility that denominator is the whole edge raw momentum has a fatal flaw. a 2% move in a calm market and a 2% move in a panic look identical to it but they are not the same signal. one is information, the other is noise wearing a big number dividing by σ_t rescales every signal into the same risk units now a move only counts as momentum if it's large relative to how much the asset is currently shaking strong drift in a quiet tape scores high. the same drift inside chaos scores near zero this is why the strategy survived the drawdown that ate buy-and-hold when volatility exploded, the denominator exploded with it, the signal shrank toward zero, and the position sized itself down automatically no rule that said "reduce risk in a crash." the math did it because σ_t was in the denominator this is called time-series momentum, and it's one of the most documented effects in finance moskowitz, ooi and pedersen, AQR, 2012: it worked across 58 markets, every asset class, back to 1900 the reason it keeps working is structural, not a pattern trends persist because information diffuses slowly and institutions can't enter all at once. a pension fund moving billions takes weeks, and that slow entry is the drift the signal captures retail buys the move and gets bigger as it accelerates, which means biggest right before the reversal a desk scales inversely to volatility, which means it's largest when the trend is clean and smallest when it's about to snap the paper is free. the whole thing is a rolling sum divided by a rolling standard deviation ten lines of python, twenty years of data that never cost anything the momentum was never the edge. everyone can see a trend the edge was dividing it by the one number that tells you whether to believe it full breakdown in the article below

delost

32,624 просмотров • 2 месяцев назад

Curt Cignetti says he’s hardest on players when his team is winning 56–10 because that's when people get content “You create the mindset, the identity of your football team and how you want them to play. Fast. Physical. Relentless. Smart. Disciplined. Poised. Never satisfied until you go to the locker room at the end of the game.” “Treat every play like it’s got a life and history of its own. Nothing-nothing. Game on the line. Get them to play that way where it’s a process. So they’re not result-oriented.” “They’re not looking at the scoreboard at the end of the game. That creates the standard. It’s all about standards. Expectations. Accountability. Process every single day.” “How you do something is how you do everything. First you form your habits. And your habits form you. You’re playing to a standard, not the scoreboard.” “When we’re up 56–10, that’s probably when I’m at my worst on the sideline. I’m looking for somebody smiling. Relaxing. We can do that in the locker room at the end of the day.” “But for 60 minutes, we’re going to play to a standard.”

Curt Cignetti says he’s hardest on players when his team is winning 56–10 because that's when people get content “You create the mindset, the identity of your football team and how you want them to play. Fast. Physical. Relentless. Smart. Disciplined. Poised. Never satisfied until you go to the locker room at the end of the game.” “Treat every play like it’s got a life and history of its own. Nothing-nothing. Game on the line. Get them to play that way where it’s a process. So they’re not result-oriented.” “They’re not looking at the scoreboard at the end of the game. That creates the standard. It’s all about standards. Expectations. Accountability. Process every single day.” “How you do something is how you do everything. First you form your habits. And your habits form you. You’re playing to a standard, not the scoreboard.” “When we’re up 56–10, that’s probably when I’m at my worst on the sideline. I’m looking for somebody smiling. Relaxing. We can do that in the locker room at the end of the day.” “But for 60 minutes, we’re going to play to a standard.”

Josh Chambers

41,361 просмотров • 2 месяцев назад

Nassim Taleb ran a simple test: take two completely random, unrelated columns of numbers - noise, nothing else - and measure their correlation. with a small sample, you'll routinely see correlations of 0.4, 0.6, even higher. from pure randomness. now the trap. give a researcher many variables and few data points - say 38 variables, 18 points each, which he says is far more common than you'd think - and let them hunt for the highest correlation. they'll always find a "strong" one. it means nothing. it's just the max of a pile of noise. "when you hear the word correlation, be suspicious." by his estimate, about half of published research is nonsense for exactly this reason. ~14-min talk, free. Taleb on the number that fools almost everyone ↓

Nassim Taleb ran a simple test: take two completely random, unrelated columns of numbers - noise, nothing else - and measure their correlation. with a small sample, you'll routinely see correlations of 0.4, 0.6, even higher. from pure randomness. now the trap. give a researcher many variables and few data points - say 38 variables, 18 points each, which he says is far more common than you'd think - and let them hunt for the highest correlation. they'll always find a "strong" one. it means nothing. it's just the max of a pile of noise. "when you hear the word correlation, be suspicious." by his estimate, about half of published research is nonsense for exactly this reason. ~14-min talk, free. Taleb on the number that fools almost everyone ↓

0xDipper

174,586 просмотров • 3 месяцев назад

This guy used Grok Bot to build a Quant Z-Score system and it made $4,654 a day on Polymarket The result is already +$525,853 PnL in 113 days with about $1,142 per average trade, repeating the same setup 11 times every hour across BTC "Up or Down" markets Multiple AI agents work together as one algorithm: 1. Z-Score measures how unusual the current move is The bot tracks short term BTC returns and compares the latest move with recent price behavior Z = (r - μ) / σ Where: r = latest BTC return μ = average return over the recent window σ = standard deviation of those returns A large positive or negative Z tells the bot that the latest move is unusually strong relative to recent volatility It uses this as an entry filter and ignores moves that stay inside the normal range 2. Expected Value determines whether the setup is worth taking EV = P(win) × Payout - Entry Cost Positive EV means the estimated payout still exceeds the cost of taking the position As the price changes, the agents recalculate EV and stop adding once the advantage disappears Most profitable trades: $4,588 → $7,905 (+$3,316) $1,578 → $4,811 (+$3,233) $2,216 → $5,305 (+$3,090) The system uses Z-Score to filter the signal and Kelly Criterion to control how much exposure each setup receives

This guy used Grok Bot to build a Quant Z-Score system and it made $4,654 a day on Polymarket The result is already +$525,853 PnL in 113 days with about $1,142 per average trade, repeating the same setup 11 times every hour across BTC "Up or Down" markets Multiple AI agents work together as one algorithm: 1. Z-Score measures how unusual the current move is The bot tracks short term BTC returns and compares the latest move with recent price behavior Z = (r - μ) / σ Where: r = latest BTC return μ = average return over the recent window σ = standard deviation of those returns A large positive or negative Z tells the bot that the latest move is unusually strong relative to recent volatility It uses this as an entry filter and ignores moves that stay inside the normal range 2. Expected Value determines whether the setup is worth taking EV = P(win) × Payout - Entry Cost Positive EV means the estimated payout still exceeds the cost of taking the position As the price changes, the agents recalculate EV and stop adding once the advantage disappears Most profitable trades: $4,588 → $7,905 (+$3,316) $1,578 → $4,811 (+$3,233) $2,216 → $5,305 (+$3,090) The system uses Z-Score to filter the signal and Kelly Criterion to control how much exposure each setup receives

Daniro

27,500 просмотров • 7 дней назад

Elon Musk explains why government efficiency matters: “I mean, North Korea, they're starving. South Korea, it's like amazing. And so in North Korea, you've got 100% government. In South Korea, you've got probably, I don't know, 40% government. It's not zero. And yet you've got a standard of living that is probably 10 times higher in South Korea. At least. And then East and West Germany, in West Germany, you had, just thinking in terms of cars, I mean, you had BMW, Porsche, Audi, Mercedes. And East Germany, which is a random line on a map, the only car you could get was a Trabant, which is basically a lawnmower with a shell on it. And it was extremely unsafe. There was a 20-year wait. So you put your kid on the list as soon as they're conceived. Even then, only, I think, a quarter of people maybe got this lousy car. So that's just an interesting example of basically the same people, different operating system.”

Elon Musk explains why government efficiency matters: “I mean, North Korea, they're starving. South Korea, it's like amazing. And so in North Korea, you've got 100% government. In South Korea, you've got probably, I don't know, 40% government. It's not zero. And yet you've got a standard of living that is probably 10 times higher in South Korea. At least. And then East and West Germany, in West Germany, you had, just thinking in terms of cars, I mean, you had BMW, Porsche, Audi, Mercedes. And East Germany, which is a random line on a map, the only car you could get was a Trabant, which is basically a lawnmower with a shell on it. And it was extremely unsafe. There was a 20-year wait. So you put your kid on the list as soon as they're conceived. Even then, only, I think, a quarter of people maybe got this lousy car. So that's just an interesting example of basically the same people, different operating system.”

Eva Fo𝕏 🦊 Claudius Nero's Legion

1,802,590 просмотров • 11 месяцев назад