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➡️ Quantitative Finance From Scratch, Part 23: Mean Reversion. Mean reversion means that a quantity which moves far from its long run average tends to drift back toward it. A single stock price behaves much like a random walk, but spreads, ratios and rates (the gap between two related...

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QuantFinanceToGo

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23,000 görüntüleme • 28 gün önce •via TikTok

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➡️ Quantitative Finance from Scratch, part 45: Statistical Arbitrage. The arbitrage from the last part was a profit without any risk. Statistical arbitrage is the practical version: many small bets that can each lose, but win on average. One of the best known and simplest forms is pairs trading: two stocks in the same business that move together. The gap between them, the spread = P_A − β · P_B (P_A price of stock A, P_B price of stock B, β the hedge ratio), keeps coming back to its average. That is the edge: the trade treats this return almost like a law, so the further the spread moves away, the more it should come back, and that way back is the profit. The rule: z = (spread − average) / standard deviation. When z reaches +2 or −2, you sell the expensive stock, buy the cheap one, and close both when the gap is back at its average. Because one stock is long and the other short, a move of the whole market mostly cancels out. News about one company can still hit a single pair, so funds make many such trades at once, and the surprises average out. The big risks: the link between two stocks can break for good, for example after a merger, and when many funds hold the same trades, they can all lose at once. The code (statarb_reel.py, 16 lines of plain Python without packages) is general on purpose: the spread from two prices and the hedge ratio, the z score, and the signal that sells the expensive stock and buys the cheap one at two standard deviations and closes at the average. If you want to go deeper, the link to my website is in my bio. #quantfinance #statisticalarbitrage #pairstrading #meanreversion #python Education only, not financial advice.
1:29

➡️ Quantitative Finance from Scratch, part 45: Statistical Arbitrage. The arbitrage from the last part was a profit without any risk. Statistical arbitrage is the practical version: many small bets that can each lose, but win on average. One of the best known and simplest forms is pairs trading: two stocks in the same business that move together. The gap between them, the spread = P_A − β · P_B (P_A price of stock A, P_B price of stock B, β the hedge ratio), keeps coming back to its average. That is the edge: the trade treats this return almost like a law, so the further the spread moves away, the more it should come back, and that way back is the profit. The rule: z = (spread − average) / standard deviation. When z reaches +2 or −2, you sell the expensive stock, buy the cheap one, and close both when the gap is back at its average. Because one stock is long and the other short, a move of the whole market mostly cancels out. News about one company can still hit a single pair, so funds make many such trades at once, and the surprises average out. The big risks: the link between two stocks can break for good, for example after a merger, and when many funds hold the same trades, they can all lose at once. The code (statarb_reel.py, 16 lines of plain Python without packages) is general on purpose: the spread from two prices and the hedge ratio, the z score, and the signal that sells the expensive stock and buys the cheap one at two standard deviations and closes at the average. If you want to go deeper, the link to my website is in my bio. #quantfinance #statisticalarbitrage #pairstrading #meanreversion #python Education only, not financial advice.

QuantFinanceToGo

37,000 görüntüleme • 2 gün önce

➡️ Quantitative Finance from scratch part 12 closes season 1. Every part was one step in a single story about a bet with an edge, and every number below comes from that part's own run. Part 1, expected value: a $1 bet on red wins 18 times out of 37, so it loses 2.7 cents on average, and over a million bets the casino made $27,288. Part 2, the random walk: coin flips add up to something that looks like a stock chart, and the same edge becomes a drift of 2.7 cents per step. Part 3, the square root rule: after 100 random steps the typical distance is about 10, because randomness grows with the square root of time. Part 4, the bell curve: many small random steps build it, with 68 % of results within one standard deviation. Part 5, returns: percent moves multiply, so after -50 % you need +100 % to get back. Part 6, present value: at 5 % interest, $100 paid in ten years is worth $61.39 today. Part 7, volatility: two investments with the same average swing about 4 % and 32 % a year. Part 8, the Sharpe ratio (William F. Sharpe, 1966): extra return per unit of risk, 0.36 for the wild investment and 0.99 for the calm one. Part 9, volatility drag: +50 % and then -50 % leaves you 25 % down. Part 10, correlation: the less two assets move together, the less a 50/50 mix swings, from 19 % down to 10 %. Part 11, diversification: ten assets cut the risk from 20 % to 12.2 %, above a floor of 11 % that the shared market sets (Markowitz, "Portfolio Selection", Journal of Finance, 1952). The big picture: how big the edge is, how much it swings, and how much of that swing you can spread away. Season 2 brings portfolios, bets and evidence. This video is for education only, and nothing in it is financial advice. #quantfinance #finance #riskmanagement #portfolio #probability
1:58

➡️ Quantitative Finance from scratch part 12 closes season 1. Every part was one step in a single story about a bet with an edge, and every number below comes from that part's own run. Part 1, expected value: a $1 bet on red wins 18 times out of 37, so it loses 2.7 cents on average, and over a million bets the casino made $27,288. Part 2, the random walk: coin flips add up to something that looks like a stock chart, and the same edge becomes a drift of 2.7 cents per step. Part 3, the square root rule: after 100 random steps the typical distance is about 10, because randomness grows with the square root of time. Part 4, the bell curve: many small random steps build it, with 68 % of results within one standard deviation. Part 5, returns: percent moves multiply, so after -50 % you need +100 % to get back. Part 6, present value: at 5 % interest, $100 paid in ten years is worth $61.39 today. Part 7, volatility: two investments with the same average swing about 4 % and 32 % a year. Part 8, the Sharpe ratio (William F. Sharpe, 1966): extra return per unit of risk, 0.36 for the wild investment and 0.99 for the calm one. Part 9, volatility drag: +50 % and then -50 % leaves you 25 % down. Part 10, correlation: the less two assets move together, the less a 50/50 mix swings, from 19 % down to 10 %. Part 11, diversification: ten assets cut the risk from 20 % to 12.2 %, above a floor of 11 % that the shared market sets (Markowitz, "Portfolio Selection", Journal of Finance, 1952). The big picture: how big the edge is, how much it swings, and how much of that swing you can spread away. Season 2 brings portfolios, bets and evidence. This video is for education only, and nothing in it is financial advice. #quantfinance #finance #riskmanagement #portfolio #probability

QuantFinanceToGo

109,700 görüntüleme • 1 ay önce

Granger and Engle won the nobel prize in economics in 2003 for a formula hedge funds were already running for a decade retail investors still don't know it exists it's called cointegration - and it answers a completely different question than what you're asking you ask "will this stock go up?" pairs desks ask "which two assets have drifted apart and must revert?" if two assets are cointegrated, their spread has a measurable half-life of mean reversion SPY and QQQ: spread half-life of roughly 4 trading days, measured over 12 years of data when that spread hits 2.1 standard deviations, reversion follows within the half-life 76% of the time that's not a hunch. it's conditional probability you can size with kelly and repeat 200 times a year paper is on jstor, data is free, implementation takes a weekend pairs desks have been doing this since the late 80s bookmark this you've been asking the wrong question your whole trading life Reply below what you think
0:47

Granger and Engle won the nobel prize in economics in 2003 for a formula hedge funds were already running for a decade retail investors still don't know it exists it's called cointegration - and it answers a completely different question than what you're asking you ask "will this stock go up?" pairs desks ask "which two assets have drifted apart and must revert?" if two assets are cointegrated, their spread has a measurable half-life of mean reversion SPY and QQQ: spread half-life of roughly 4 trading days, measured over 12 years of data when that spread hits 2.1 standard deviations, reversion follows within the half-life 76% of the time that's not a hunch. it's conditional probability you can size with kelly and repeat 200 times a year paper is on jstor, data is free, implementation takes a weekend pairs desks have been doing this since the late 80s bookmark this you've been asking the wrong question your whole trading life Reply below what you think

Livsun

11,624 görüntüleme • 4 ay önce

Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.
40:58

Alan Oppenheim, MIT professor: "Every trader alive uses a moving average. Almost none of them know it's a filter, and funds pay quants $600K for the version they were never taught." a moving average is a filter. it smooths a jumpy price to reveal the slower signal hiding underneath. that is all it is, and it is the bluntest one there is. the same math gives you filters that lag less and cut more noise, and that gap is a real edge. this is the second half of what Simons was doing. first you accept the market is a signal buried in noise. then you build the filter that reads it, and the moving average on your chart is the crudest possible version. Oppenheim builds the real ones at the board in this lecture. here is the catch. a filter only helps if there is a real signal under the noise. smooth pure randomness and you will see a beautiful trend that means nothing. the filter is free. knowing there is a real signal worth filtering is the edge.

Rossst.03

214,907 görüntüleme • 4 ay önce

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.
1:23:57

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.

Veles

2,191,653 görüntüleme • 4 ay önce

Flip a coin. Heads, your account goes up 50 percent. Tails, it goes down 40 percent. Expected value is plus 5 percent a flip, so you take the bet a hundred times. Expected value says your $10,000 becomes $1.3 million. The most likely path leaves you with $52. An MIT professor explains the entire gap in one sentence, in a free undergraduate lecture, then moves on like it was nothing. His name is John Tsitsiklis. He teaches undergraduate probability at MIT. He also proved in 1994 that Q-learning converges, the result that says the algorithm under modern reinforcement learning does not merely happen to work, it has to. INFORMS gave him the von Neumann Theory Prize for that line of work in 2018. He runs the lecture on the students for an hour. First he takes the average apart. A random variable is not a number, it is a function. A bar graph of probabilities is a PMF. Expectation is the center of gravity of that bar graph, the single point where you slide a pen underneath and the thing balances. He is slow and patient about it. By minute 35 you trust the average completely. Then he stops and says he wants to give "one general word of caution." "The average of a function of a random variable, in general, is not the same as the function of the average... in general, you can not reason on the average." Everything before that sentence was the trap. Go back to the coin. Compounding is not addition. Up 50 then down 40 is not plus 10. It is 1.5 times 0.6, which is 0.9. You are down 10 percent. Do that 50 times each way and you have 0.9 to the fiftieth power. Fifty-two dollars. So where did the $1.3 million go? It is real. It is parked at the very top of the distribution. Run the hundred flips and only about one path in seven finishes above where you started. Only about one in a hundred ever reaches that $1.3 million. Those few runs are gigantic, and they carry the average for everybody else. You will not be in them. In February 2018 that trade had a ticker. XIV, short volatility, $1.9 billion in it. It had paid on the average day for seven years. On February 5 the VIX rose 115.6 percent, the biggest one-day jump ever recorded. XIV went from $115.55 to $4.22 overnight. Credit Suisse shut the note two weeks later. Nobody in it was wrong about the average. They were wrong about which path they were standing on. The usable version: your compound return is your average return minus roughly half your variance. A system averaging 20 percent a year at 40 percent vol does not compound at 20. It compounds at 12. That missing 8 is not fees or slippage. Tsitsiklis delivers the most expensive sentence in finance, finishes the variance section, and ends with "see you on Wednesday." The lecture is free. The average is free. Knowing which path you are standing on is the trade.
47:45

Flip a coin. Heads, your account goes up 50 percent. Tails, it goes down 40 percent. Expected value is plus 5 percent a flip, so you take the bet a hundred times. Expected value says your $10,000 becomes $1.3 million. The most likely path leaves you with $52. An MIT professor explains the entire gap in one sentence, in a free undergraduate lecture, then moves on like it was nothing. His name is John Tsitsiklis. He teaches undergraduate probability at MIT. He also proved in 1994 that Q-learning converges, the result that says the algorithm under modern reinforcement learning does not merely happen to work, it has to. INFORMS gave him the von Neumann Theory Prize for that line of work in 2018. He runs the lecture on the students for an hour. First he takes the average apart. A random variable is not a number, it is a function. A bar graph of probabilities is a PMF. Expectation is the center of gravity of that bar graph, the single point where you slide a pen underneath and the thing balances. He is slow and patient about it. By minute 35 you trust the average completely. Then he stops and says he wants to give "one general word of caution." "The average of a function of a random variable, in general, is not the same as the function of the average... in general, you can not reason on the average." Everything before that sentence was the trap. Go back to the coin. Compounding is not addition. Up 50 then down 40 is not plus 10. It is 1.5 times 0.6, which is 0.9. You are down 10 percent. Do that 50 times each way and you have 0.9 to the fiftieth power. Fifty-two dollars. So where did the $1.3 million go? It is real. It is parked at the very top of the distribution. Run the hundred flips and only about one path in seven finishes above where you started. Only about one in a hundred ever reaches that $1.3 million. Those few runs are gigantic, and they carry the average for everybody else. You will not be in them. In February 2018 that trade had a ticker. XIV, short volatility, $1.9 billion in it. It had paid on the average day for seven years. On February 5 the VIX rose 115.6 percent, the biggest one-day jump ever recorded. XIV went from $115.55 to $4.22 overnight. Credit Suisse shut the note two weeks later. Nobody in it was wrong about the average. They were wrong about which path they were standing on. The usable version: your compound return is your average return minus roughly half your variance. A system averaging 20 percent a year at 40 percent vol does not compound at 20. It compounds at 12. That missing 8 is not fees or slippage. Tsitsiklis delivers the most expensive sentence in finance, finishes the variance section, and ends with "see you on Wednesday." The lecture is free. The average is free. Knowing which path you are standing on is the trade.

Veles

635,710 görüntüleme • 3 ay önce

Yilin Wang, MIT mathematician and one of the leading minds on the geometry of randomness: "there is a precise number for how far a shape is from pure chance. Citadel pays $500,000 to people who compute it instead of guessing it." her field is the mathematics of random curves. flip enough coins and the path they trace is not shapeless. it has a precise geometry, and a number, the Loewner energy, that measures how far a curve sits from pure chance. a circle scores zero. it is the most natural thing randomness can draw. almost every shape you see on a chart scores near zero too. it is noise wearing a costume. this is the entire quant problem in one idea. the market draws millions of random curves, and only a vanishing few carry real structure. the edge is a rigorous way to tell the costume from the signal. Citadel pay $500,000 a year for people who can measure that difference instead of feeling it. the trader who falls in love with a pattern is admiring a curve that randomness would have drawn anyway. the shape was never the edge. the odds that randomness did not draw it were.
1:50:26

Yilin Wang, MIT mathematician and one of the leading minds on the geometry of randomness: "there is a precise number for how far a shape is from pure chance. Citadel pays $500,000 to people who compute it instead of guessing it." her field is the mathematics of random curves. flip enough coins and the path they trace is not shapeless. it has a precise geometry, and a number, the Loewner energy, that measures how far a curve sits from pure chance. a circle scores zero. it is the most natural thing randomness can draw. almost every shape you see on a chart scores near zero too. it is noise wearing a costume. this is the entire quant problem in one idea. the market draws millions of random curves, and only a vanishing few carry real structure. the edge is a rigorous way to tell the costume from the signal. Citadel pay $500,000 a year for people who can measure that difference instead of feeling it. the trader who falls in love with a pattern is admiring a curve that randomness would have drawn anyway. the shape was never the edge. the odds that randomness did not draw it were.

Rossst.03

59,310 görüntüleme • 2 ay önce

➡️ Part 25 of Quantitative Finance From Scratch: the value factor. A value stock has a low price to book ratio; researchers flip it to book to market and compare the cheapest stocks with the most expensive ones. In our simulation (500 simulated stocks, 30 years, assumed numbers) the value portfolio earns 4.7 % a year with a t statistic of 4.29. It is a model with a small built-in premium, so it shows how the method works, not what real markets will pay. The chart in the hook is a schematic, not real data. Value and momentum have tended to move in opposite directions in real markets. In our simulation they are almost unrelated (correlation 0.04), and the 50/50 mix is still smoother than either alone. Education only, not financial advice. #quantfinance #valueinvesting #factorinvesting #python #finance
1:45

➡️ Part 25 of Quantitative Finance From Scratch: the value factor. A value stock has a low price to book ratio; researchers flip it to book to market and compare the cheapest stocks with the most expensive ones. In our simulation (500 simulated stocks, 30 years, assumed numbers) the value portfolio earns 4.7 % a year with a t statistic of 4.29. It is a model with a small built-in premium, so it shows how the method works, not what real markets will pay. The chart in the hook is a schematic, not real data. Value and momentum have tended to move in opposite directions in real markets. In our simulation they are almost unrelated (correlation 0.04), and the 50/50 mix is still smoother than either alone. Education only, not financial advice. #quantfinance #valueinvesting #factorinvesting #python #finance

QuantFinanceToGo

13,100 görüntüleme • 26 gün önce

Life hack for helping a dog with a wound. We learned this from a vet. Cut a tube sock in half. Place it on the paw and tie it on the shoulders. The dog can still easily move around but can’t just pull the bandage off. #dogsoftiktok #doglove #lifehack
0:45

Life hack for helping a dog with a wound. We learned this from a vet. Cut a tube sock in half. Place it on the paw and tie it on the shoulders. The dog can still easily move around but can’t just pull the bandage off. #dogsoftiktok #doglove #lifehack

2HappyDogsAndaTeacher

159,000 görüntüleme • 9 ay önce

Kevin Hart says life is a game of Monopoly and invests 25% of his earnings in real estate “The game of Monopoly is a real game. The goal is to buy up as much property as you can and get people to pay you for that property when they land on it” “Rent. That’s the fucking game” “Let’s say I make a nice amount of money to do a movie. Split that number in half. Half of that goes to the government, the IRS" “Create a fucking account. Call it the tax account. I never worry about giving the government their money because that money’s over there” “So now, off of my half, what do I want to do? I’m gonna take another half. This half is for my next real estate investment” “I’m only living off a quarter of my financial gain. You only get in debt when you don’t understand the breakdown of your finances”
1:48

Kevin Hart says life is a game of Monopoly and invests 25% of his earnings in real estate “The game of Monopoly is a real game. The goal is to buy up as much property as you can and get people to pay you for that property when they land on it” “Rent. That’s the fucking game” “Let’s say I make a nice amount of money to do a movie. Split that number in half. Half of that goes to the government, the IRS" “Create a fucking account. Call it the tax account. I never worry about giving the government their money because that money’s over there” “So now, off of my half, what do I want to do? I’m gonna take another half. This half is for my next real estate investment” “I’m only living off a quarter of my financial gain. You only get in debt when you don’t understand the breakdown of your finances”

dank

444,683 görüntüleme • 5 ay önce

➡️ Quantitative Finance from Scratch, part 39: GARCH. Markets show a clear pattern: calm weeks follow calm weeks, and high volatility days arrive in groups. This is called volatility clustering. Robert Engle introduced the idea in 1982 with the ARCH model (Econometrica), Tim Bollerslev extended it to GARCH in 1986 (Journal of Econometrics), and Engle received the 2003 Nobel Prize in economics for methods of analyzing time series with time-varying volatility. GARCH(1,1) builds tomorrow's variance, the square of volatility, from three parts: a small constant omega that pulls the forecast back toward a normal level, today's move squared times a weight alpha, so a big surprise raises tomorrow's forecast right away, and today's variance times a weight beta, the memory that keeps a high forecast high for a while. The code (garch_reel.py, 16 lines of Python with numpy) uses typical values omega 0.05, alpha 0.10 and beta 0.85, in percent squared per day. The normal level is a volatility of 1 % a day. After a drop of 4 % on a calm day, tomorrow's variance is 0.05 + 0.10 x 16 + 0.85 x 1 = 2.50, so the forecast volatility jumps to 1.58 %. The extra variance then halves every 13.5 trading days: the forecast for day 5 is 1.49 %, for day 10 1.39 % and for day 30 1.16 %. In 1,000,000 simulated days of the same model, a move above 2.5 % happens on 1.75 % of all days, but on 10.97 % of the days right after such a move, about 6.3 times as often. Risk managers use GARCH forecasts to update value at risk, and some funds scale their positions down when the forecast volatility rises. If you want the full picture, there is a longer GARCH video on the YouTube channel. Honest notes: all data in the video is simulated with the GARCH model itself, so the clustering is built in by design; in real markets volatility usually reacts more strongly to falls than to rises, which this simple symmetric model leaves out. The 3D landscape of columns (20 simulated stocks sharing one GARCH volatility) is an illustration with compressed heights, not to scale. The value at risk line uses 1.65 times the forecast volatility, a normal approximation for one day at 95 %. #quantfinance #garch #volatility #riskmanagement #finance Education only, not financial advice.
1:40

➡️ Quantitative Finance from Scratch, part 39: GARCH. Markets show a clear pattern: calm weeks follow calm weeks, and high volatility days arrive in groups. This is called volatility clustering. Robert Engle introduced the idea in 1982 with the ARCH model (Econometrica), Tim Bollerslev extended it to GARCH in 1986 (Journal of Econometrics), and Engle received the 2003 Nobel Prize in economics for methods of analyzing time series with time-varying volatility. GARCH(1,1) builds tomorrow's variance, the square of volatility, from three parts: a small constant omega that pulls the forecast back toward a normal level, today's move squared times a weight alpha, so a big surprise raises tomorrow's forecast right away, and today's variance times a weight beta, the memory that keeps a high forecast high for a while. The code (garch_reel.py, 16 lines of Python with numpy) uses typical values omega 0.05, alpha 0.10 and beta 0.85, in percent squared per day. The normal level is a volatility of 1 % a day. After a drop of 4 % on a calm day, tomorrow's variance is 0.05 + 0.10 x 16 + 0.85 x 1 = 2.50, so the forecast volatility jumps to 1.58 %. The extra variance then halves every 13.5 trading days: the forecast for day 5 is 1.49 %, for day 10 1.39 % and for day 30 1.16 %. In 1,000,000 simulated days of the same model, a move above 2.5 % happens on 1.75 % of all days, but on 10.97 % of the days right after such a move, about 6.3 times as often. Risk managers use GARCH forecasts to update value at risk, and some funds scale their positions down when the forecast volatility rises. If you want the full picture, there is a longer GARCH video on the YouTube channel. Honest notes: all data in the video is simulated with the GARCH model itself, so the clustering is built in by design; in real markets volatility usually reacts more strongly to falls than to rises, which this simple symmetric model leaves out. The 3D landscape of columns (20 simulated stocks sharing one GARCH volatility) is an illustration with compressed heights, not to scale. The value at risk line uses 1.65 times the forecast volatility, a normal approximation for one day at 95 %. #quantfinance #garch #volatility #riskmanagement #finance Education only, not financial advice.

QuantFinanceToGo

71,900 görüntüleme • 9 gün önce

Pitching is, fundamentally, a rotational movement.⁠ ⁠ Yes, there is a linear component, but a pitcher must ultimately be able to transfer that into rotation for that energy to efficiently make its way from the ground and into the ball.🔑⁠ ⁠ How does that energy work its way from the GRF on the back foot into the upper half among the most efficient throwers?⁠ ⁠ Is it a "car crash" where the back leg violently extends, followed by a front leg block that linearly catapults the upper half forwards?⁠ ⁠ A careful analysis of the hardest throwers reveals that this linear view of the lower half is incomplete.⁠ ⁠ 1/
0:21

Pitching is, fundamentally, a rotational movement.⁠ ⁠ Yes, there is a linear component, but a pitcher must ultimately be able to transfer that into rotation for that energy to efficiently make its way from the ground and into the ball.🔑⁠ ⁠ How does that energy work its way from the GRF on the back foot into the upper half among the most efficient throwers?⁠ ⁠ Is it a "car crash" where the back leg violently extends, followed by a front leg block that linearly catapults the upper half forwards?⁠ ⁠ A careful analysis of the hardest throwers reveals that this linear view of the lower half is incomplete.⁠ ⁠ 1/

Tread Athletics

28,887 görüntüleme • 5 ay önce

🚨BREAKING: Claude can now build a instagram page from scratch — and hit monetization in just 90 days. 100% free Here is exactly how I would make that happen Imagine how much your life would change if you had a page with 500k followers. You could post one time and over half a million people would see exactly what you are thinking. Not only is it valuable to you but to millions of companies all over the world. The best part this is like buying real estate in the 1980s or crypto in 2014 I am going All in on Social media over 6 Billion people use social media every day but only 1% of people using social media create it. So even if that number doubled you would still be ahead of 98% of the population. Don’t look back on this in 5 years and say I wish I would have done that. Make a promise to yourself and change your life today.
1:36

🚨BREAKING: Claude can now build a instagram page from scratch — and hit monetization in just 90 days. 100% free Here is exactly how I would make that happen Imagine how much your life would change if you had a page with 500k followers. You could post one time and over half a million people would see exactly what you are thinking. Not only is it valuable to you but to millions of companies all over the world. The best part this is like buying real estate in the 1980s or crypto in 2014 I am going All in on Social media over 6 Billion people use social media every day but only 1% of people using social media create it. So even if that number doubled you would still be ahead of 98% of the population. Don’t look back on this in 5 years and say I wish I would have done that. Make a promise to yourself and change your life today.

mika kilpelainen

57,000 görüntüleme • 1 ay önce

This is Half-Life: Alyx – running natively on my Quest 3 standalone 🤯 Modder Zer0K, the creator of the Half-Life 2 VR port for Quest, has gone a step further and decided to bring the latest installment in the series - and one of the most famous VR games, HL: Alyx - to the Meta's standalone headset. He just released an early test version and now everyone can see for themselves what the Quest 3 is capable of 😮 Keep in mind, however, that this isn't the final version of the mod yet, it isn't fully playable and may suffer from frame drops - though it is a complete port of the PC version. At this stage, it serves more as a proof of concept, demonstrating that something like this can work even on a low-power, three-year-old headset. The modder hopes that this mod has good potential for future optimization and will definitely perform better. This is also proof that we live in times where everything is possible for VR 🤩 An early test version of the mod can be downloaded from here, of course it will require the original Half-Life: Alyx PCVR files and a lot of free space on your Quest's hard drive - prepare at least 70 (!) GB:
6:58

This is Half-Life: Alyx – running natively on my Quest 3 standalone 🤯 Modder Zer0K, the creator of the Half-Life 2 VR port for Quest, has gone a step further and decided to bring the latest installment in the series - and one of the most famous VR games, HL: Alyx - to the Meta's standalone headset. He just released an early test version and now everyone can see for themselves what the Quest 3 is capable of 😮 Keep in mind, however, that this isn't the final version of the mod yet, it isn't fully playable and may suffer from frame drops - though it is a complete port of the PC version. At this stage, it serves more as a proof of concept, demonstrating that something like this can work even on a low-power, three-year-old headset. The modder hopes that this mod has good potential for future optimization and will definitely perform better. This is also proof that we live in times where everything is possible for VR 🤩 An early test version of the mod can be downloaded from here, of course it will require the original Half-Life: Alyx PCVR files and a lot of free space on your Quest's hard drive - prepare at least 70 (!) GB:

Hostile VR

60,740 görüntüleme • 1 ay önce

Americans say they aren’t being paid enough to be a Nanny for the amount of work that’s required “You want us to be a house cleaner, a cook, um, a chauffeur, a dog watcher and walker, and you think that $18 an hour is acceptable” This is in California, and I found that this isn’t too far off from the average The statewide average for nanny rates in California is about $22 an hour on the lower end unless you have a lot of experience or get lucky with a higher payer Compare that to daycare costs and it’s about $1,300 per 2 weeks for full time and up to $900 for 2 weeks part time
1:28

Americans say they aren’t being paid enough to be a Nanny for the amount of work that’s required “You want us to be a house cleaner, a cook, um, a chauffeur, a dog watcher and walker, and you think that $18 an hour is acceptable” This is in California, and I found that this isn’t too far off from the average The statewide average for nanny rates in California is about $22 an hour on the lower end unless you have a lot of experience or get lucky with a higher payer Compare that to daycare costs and it’s about $1,300 per 2 weeks for full time and up to $900 for 2 weeks part time

Wall Street Apes

272,920 görüntüleme • 4 ay önce

➡️ Quantitative Finance from Scratch, part 35: how market makers make money. The first source is the spread. Like an exchange booth, a market maker buys a little below the fair price at the bid and sells a little above it at the ask, so every trade earns half the gap whatever the stock does next. After selling a call, it buys delta shares, so small moves no longer matter. The second source comes from the other Greeks: theta is the income, because the call it owes loses value every day, and gamma is the cost, because after every big move it must buy shares high and sell them low to keep the hedge. If the stock moves exactly as much as the implied volatility in the price, income and cost cancel. If it stays calmer, the market maker earns, and if it moves more, it loses. Vega is the risk to this income, because a jump in implied volatility makes the call it owes more expensive. If a customer sells it the call instead, everything flips. The code (marketmaker.py, 16 lines) sells our call from part 33 at the ask (fair $10.45, ask $10.55, bid $10.35) and hedges it every day on 2,000 simulated years with an implied volatility of 20 %. With a real volatility of 16 % it earns $1.59 per call on average and every path ends in profit; at 20 % it earns $0.10, about the spread; at 24 % it loses $1.41 and only 0.3 % of paths end in profit. Honest notes: a simplified model with one option, daily hedging, Black Scholes prices, constant volatility, no trading costs, no dividends and no other customers. Real market makers manage many options at once and also face fees, inventory limits and jumps. #quantfinance #options #marketmaking #greeks #finance
2:35

➡️ Quantitative Finance from Scratch, part 35: how market makers make money. The first source is the spread. Like an exchange booth, a market maker buys a little below the fair price at the bid and sells a little above it at the ask, so every trade earns half the gap whatever the stock does next. After selling a call, it buys delta shares, so small moves no longer matter. The second source comes from the other Greeks: theta is the income, because the call it owes loses value every day, and gamma is the cost, because after every big move it must buy shares high and sell them low to keep the hedge. If the stock moves exactly as much as the implied volatility in the price, income and cost cancel. If it stays calmer, the market maker earns, and if it moves more, it loses. Vega is the risk to this income, because a jump in implied volatility makes the call it owes more expensive. If a customer sells it the call instead, everything flips. The code (marketmaker.py, 16 lines) sells our call from part 33 at the ask (fair $10.45, ask $10.55, bid $10.35) and hedges it every day on 2,000 simulated years with an implied volatility of 20 %. With a real volatility of 16 % it earns $1.59 per call on average and every path ends in profit; at 20 % it earns $0.10, about the spread; at 24 % it loses $1.41 and only 0.3 % of paths end in profit. Honest notes: a simplified model with one option, daily hedging, Black Scholes prices, constant volatility, no trading costs, no dividends and no other customers. Real market makers manage many options at once and also face fees, inventory limits and jumps. #quantfinance #options #marketmaking #greeks #finance

QuantFinanceToGo

93,300 görüntüleme • 13 gün önce

The Zionist entity is a failed experiment. It is on life-support – pull those three plugs and it is a cadaver, living only in the minds of some seriously deluded foreigners who thought they could pull off the heist of the century.
0:52

The Zionist entity is a failed experiment. It is on life-support – pull those three plugs and it is a cadaver, living only in the minds of some seriously deluded foreigners who thought they could pull off the heist of the century.

Haitham Abdelhadi

16,879 görüntüleme • 9 ay önce

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm
2:03

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm

MachineLearningToGo

79,400 görüntüleme • 1 ay önce