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this model doesn't predict where the S&P 500 will go it predicts when the market's own velocity is about to collapse Z(x,y) = F(β, α, τ, ∇τ; x, y, t) one equation. four parameters. a 3D surface that maps how price momentum evolves across time and space β is...

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72,275 görüntüleme • 2 ay önce •via X (Twitter)

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String Theory Lecture 1 A String Does Not Move Like a Point A point particle traces a line through spacetime. A string traces a surface. This is the first geometric shift in String Theory. Particle mechanics asks where one object is at time t, so its history is a curve. String Theory asks where every point of an extended object is at worldsheet time τ, so we need another coordinate telling us where we are along the string. For a point particle x(t) So, for one input of time we get a position in Spacetime. For a string Xᵘ(τ,σ) Here τ plays the role of time on the worldsheet, while σ labels position along the string. Freeze τ and vary σ, and you see the string at one instant. Let τ move, and that curve sweeps out a two-dimensional surface... the worldsheet. The same comparison appears in the action. For a relativistic point particle, the geometric action measures worldline length S = −m ∫ ds If we parameterize the path by t, the action has one integral, one parameter, and one tangent vector dxᵘ/dt For a string, the same idea grows by one dimension. The action measures area, not length. In Nambu-Goto form, S = −T ∫ dτ dσ √[−det hₐᵦ] Here T is the string tension. It plays a role similar to mass, but for an extended object. It weights the area of a surface rather than the length of a line. The particle action has ∫ dt because the history is one-dimensional. The string action has ∫ dτ dσ because the history is two-dimensional. We are no longer summing along a path, we are summing over a surface. The geometry changes for the same reason. For the particle, one derivative is enough dxᵘ/dt For the string, the geometry is built from two derivatives: ∂τXᵘ and ∂σXᵘ The first tells you how the string changes as worldsheet time flows. The second tells you how the embedding changes as you move along the string. Together they define the induced worldsheet metric hₐᵦ = ∂ₐXᵘ ∂ᵦXᵤ In plain terms, hₐᵦ measures tangent lengths and tangent angles on the worldsheet. From it, the area element is dA = dτ dσ √[−det hₐᵦ] This, the Nambu-Goto action is the direct analogue of the point-particle length action. The point particle extremizes length and the string extremizes area. For calculations, people usually switch to the Polyakov action: S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ This describes the same classical string dynamics, but the algebra is cleaner. After choosing conformal gauge, varying with respect to Xᵘ gives (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 This is the first real dynamical payoff... a two-dimensional wave equation on the worldsheet. For a point particle, the equation of motion tells you how one position evolves along one path. For a string, it tells you how an entire curve evolves, with waves traveling along it. The term ∂²Xᵘ/∂τ² measures acceleration in worldsheet time, while ∂²Xᵘ/∂σ² measures curvature along the string. The time evolution is balanced by how the string bends along its own length. This is why strings have oscillation modes. A point particle has one trajectory. A string has many possible vibration patterns, each one a normal mode of the worldsheet wave equation. For a closed string, σ wraps around the loop Xᵘ(τ, σ + 2π) = Xᵘ(τ, σ) For an open string, one standard free-end condition is ∂σXᵘ = 0 at the endpoints. Solving the wave equation gives waves moving in opposite directions along the string Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) A function of τ + σ moves one way. A function of τ − σ moves the other. Therefore, a particle has a worldline, its action measures length, and its geometry uses one tangent. The string has a worldsheet, its action measures area, and its geometry uses two tangent directions. #StringTheory #TheoreticalPhysics #MathematicalPhysics #Physics #Spacetime
0:34

String Theory Lecture 1 A String Does Not Move Like a Point A point particle traces a line through spacetime. A string traces a surface. This is the first geometric shift in String Theory. Particle mechanics asks where one object is at time t, so its history is a curve. String Theory asks where every point of an extended object is at worldsheet time τ, so we need another coordinate telling us where we are along the string. For a point particle x(t) So, for one input of time we get a position in Spacetime. For a string Xᵘ(τ,σ) Here τ plays the role of time on the worldsheet, while σ labels position along the string. Freeze τ and vary σ, and you see the string at one instant. Let τ move, and that curve sweeps out a two-dimensional surface... the worldsheet. The same comparison appears in the action. For a relativistic point particle, the geometric action measures worldline length S = −m ∫ ds If we parameterize the path by t, the action has one integral, one parameter, and one tangent vector dxᵘ/dt For a string, the same idea grows by one dimension. The action measures area, not length. In Nambu-Goto form, S = −T ∫ dτ dσ √[−det hₐᵦ] Here T is the string tension. It plays a role similar to mass, but for an extended object. It weights the area of a surface rather than the length of a line. The particle action has ∫ dt because the history is one-dimensional. The string action has ∫ dτ dσ because the history is two-dimensional. We are no longer summing along a path, we are summing over a surface. The geometry changes for the same reason. For the particle, one derivative is enough dxᵘ/dt For the string, the geometry is built from two derivatives: ∂τXᵘ and ∂σXᵘ The first tells you how the string changes as worldsheet time flows. The second tells you how the embedding changes as you move along the string. Together they define the induced worldsheet metric hₐᵦ = ∂ₐXᵘ ∂ᵦXᵤ In plain terms, hₐᵦ measures tangent lengths and tangent angles on the worldsheet. From it, the area element is dA = dτ dσ √[−det hₐᵦ] This, the Nambu-Goto action is the direct analogue of the point-particle length action. The point particle extremizes length and the string extremizes area. For calculations, people usually switch to the Polyakov action: S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ This describes the same classical string dynamics, but the algebra is cleaner. After choosing conformal gauge, varying with respect to Xᵘ gives (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 This is the first real dynamical payoff... a two-dimensional wave equation on the worldsheet. For a point particle, the equation of motion tells you how one position evolves along one path. For a string, it tells you how an entire curve evolves, with waves traveling along it. The term ∂²Xᵘ/∂τ² measures acceleration in worldsheet time, while ∂²Xᵘ/∂σ² measures curvature along the string. The time evolution is balanced by how the string bends along its own length. This is why strings have oscillation modes. A point particle has one trajectory. A string has many possible vibration patterns, each one a normal mode of the worldsheet wave equation. For a closed string, σ wraps around the loop Xᵘ(τ, σ + 2π) = Xᵘ(τ, σ) For an open string, one standard free-end condition is ∂σXᵘ = 0 at the endpoints. Solving the wave equation gives waves moving in opposite directions along the string Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) A function of τ + σ moves one way. A function of τ − σ moves the other. Therefore, a particle has a worldline, its action measures length, and its geometry uses one tangent. The string has a worldsheet, its action measures area, and its geometry uses two tangent directions. #StringTheory #TheoreticalPhysics #MathematicalPhysics #Physics #Spacetime

Mathelirium

31,560 görüntüleme • 3 ay önce

String Theory Lecture 2 In Conformal Gauge, the String Becomes a Wave Equation Episode 1 showed the geometric jump point particle -> worldline string -> worldsheet Episode 2 is the dynamical jump. The Nambu-Goto action measures the area of the worldsheet, S = −T ∫ dτ dσ √[−det hₐᵦ] but the square-root determinant is awkward to work with. So we usually rewrite the same classical theory in Polyakov form, S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ Here Xᵘ(τ,σ) tells us where each point of the string’s worldsheet sits in spacetime, and γₐᵦ is the metric we put on the worldsheet. The power of this form is that we can choose a convenient gauge. In conformal gauge, the equations of motion simplify to (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 So the string’s spacetime coordinates behave like waves living on the worldsheet. The τ-derivative measures how the string changes in worldsheet time. The σ-derivative measures how it bends along its own length. For a closed string, σ is periodic Xᵘ(τ, σ + 2π) = Xᵘ(τ,σ) and the wave equation splits into two traveling pieces, Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) One family moves one way around the string and the other moves the opposite way. These are the left-moving and right-moving modes. In the render, the bright loop is the string at the present moment. The glowing cylinder behind it is the worldsheet it has swept out. The cyan curves trace one traveling family, and the gold curves trace the other. They are the visual version of τ + σ and τ − σ. Therefore, the theory has an internal wave equation, and its normal modes are the raw material for the string spectrum. #StringTheory #TheoreticalPhysics #ConformalGauge #Worldsheet #WaveEquation #Physics #Mathematics #MathematicalPhysics #QuantumGravity #ScienceVisuals
0:30

String Theory Lecture 2 In Conformal Gauge, the String Becomes a Wave Equation Episode 1 showed the geometric jump point particle -> worldline string -> worldsheet Episode 2 is the dynamical jump. The Nambu-Goto action measures the area of the worldsheet, S = −T ∫ dτ dσ √[−det hₐᵦ] but the square-root determinant is awkward to work with. So we usually rewrite the same classical theory in Polyakov form, S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ Here Xᵘ(τ,σ) tells us where each point of the string’s worldsheet sits in spacetime, and γₐᵦ is the metric we put on the worldsheet. The power of this form is that we can choose a convenient gauge. In conformal gauge, the equations of motion simplify to (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 So the string’s spacetime coordinates behave like waves living on the worldsheet. The τ-derivative measures how the string changes in worldsheet time. The σ-derivative measures how it bends along its own length. For a closed string, σ is periodic Xᵘ(τ, σ + 2π) = Xᵘ(τ,σ) and the wave equation splits into two traveling pieces, Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) One family moves one way around the string and the other moves the opposite way. These are the left-moving and right-moving modes. In the render, the bright loop is the string at the present moment. The glowing cylinder behind it is the worldsheet it has swept out. The cyan curves trace one traveling family, and the gold curves trace the other. They are the visual version of τ + σ and τ − σ. Therefore, the theory has an internal wave equation, and its normal modes are the raw material for the string spectrum. #StringTheory #TheoreticalPhysics #ConformalGauge #Worldsheet #WaveEquation #Physics #Mathematics #MathematicalPhysics #QuantumGravity #ScienceVisuals

Mathelirium

15,607 görüntüleme • 3 ay önce

Phase Space Is the Real Stage of Dynamics. So, Stop Watching the Object and Watch the State. Ordinary Space tells you where something is. Phase Space tells you what state it is in. Position alone is not enough because you also need the variable that tells you where the system is trying to go. In the animation, the left panel shows the particle moving in Ordinary Space, while the right panel shows the same system as a moving point in Phase Space. For one degree of freedom, the state is (x, p) and as time evolves, that state traces a trajectory t ↦ (x(t), p(t)) Instead of thinking of a differential equation as just a formula, Phase Space lets you see it as a flow on the space of states. For this lecture we use ẋ = p ṗ = x − x³ with Hamiltonian H(x,p) = 0.5 p² + 0.25 x⁴ − 0.5 x² #PhaseSpace #DynamicalSystems #NonlinearDynamics #HamiltonianMechanics #Mathematics
0:30

Phase Space Is the Real Stage of Dynamics. So, Stop Watching the Object and Watch the State. Ordinary Space tells you where something is. Phase Space tells you what state it is in. Position alone is not enough because you also need the variable that tells you where the system is trying to go. In the animation, the left panel shows the particle moving in Ordinary Space, while the right panel shows the same system as a moving point in Phase Space. For one degree of freedom, the state is (x, p) and as time evolves, that state traces a trajectory t ↦ (x(t), p(t)) Instead of thinking of a differential equation as just a formula, Phase Space lets you see it as a flow on the space of states. For this lecture we use ẋ = p ṗ = x − x³ with Hamiltonian H(x,p) = 0.5 p² + 0.25 x⁴ − 0.5 x² #PhaseSpace #DynamicalSystems #NonlinearDynamics #HamiltonianMechanics #Mathematics

Mathelirium

30,447 görüntüleme • 4 ay önce

a 25-delta put trades at 22% implied vol. the call at the same distance trades at 15% same index, same expiry, same distance from the money. 7 points apart black-scholes says that gap shouldn't exist the model assumes one volatility number for every strike σ constant across strikes and expiries. one distribution, lognormal returns, flat surface plot the real chain and you don't get a flat line you get a surface. tilted, curved, repricing every second that tilt has a name: skew and it's the closest thing markets have to a live fear gauge the reason it exists is structural, not a pricing error crashes are faster and deeper than rallies. returns have fat left tails so protection below the market costs more than the model says, because the model's normal distribution never priced the tail correctly this wasn't always true before october 1987 the surface was roughly flat. the crash rewrote it permanently one event taught the entire options market that the left tail is real, and the skew has never gone away since the measurement is simple: skew = IV(25-delta put) − IV(25-delta call) on the example above that's 22 − 15 = 7 points when that spread widens, demand for downside protection is rising. someone is paying up to hedge when it flattens, the bid for protection is fading same index level, same price on your chart, two completely different states of institutional fear the chart shows where price is. the surface shows what people are paying to be wrong and the shape carries more than direction steepness tells you how much tail risk is priced term structure tells you whether the fear is about this week or this quarter curvature tells you how much the market disagrees with its own base case retail sees one implied vol number on the option they're about to buy a desk sees a surface and trades the difference between its shape and what that shape usually looks like every input is public. the chain lists IV at every strike and expiry strike on one axis, expiry on another, IV on the third. the surface builds itself black-scholes won a nobel for a formula the market has been visibly disagreeing with since 1987 the disagreement is the signal full breakdown in the article below
0:58

a 25-delta put trades at 22% implied vol. the call at the same distance trades at 15% same index, same expiry, same distance from the money. 7 points apart black-scholes says that gap shouldn't exist the model assumes one volatility number for every strike σ constant across strikes and expiries. one distribution, lognormal returns, flat surface plot the real chain and you don't get a flat line you get a surface. tilted, curved, repricing every second that tilt has a name: skew and it's the closest thing markets have to a live fear gauge the reason it exists is structural, not a pricing error crashes are faster and deeper than rallies. returns have fat left tails so protection below the market costs more than the model says, because the model's normal distribution never priced the tail correctly this wasn't always true before october 1987 the surface was roughly flat. the crash rewrote it permanently one event taught the entire options market that the left tail is real, and the skew has never gone away since the measurement is simple: skew = IV(25-delta put) − IV(25-delta call) on the example above that's 22 − 15 = 7 points when that spread widens, demand for downside protection is rising. someone is paying up to hedge when it flattens, the bid for protection is fading same index level, same price on your chart, two completely different states of institutional fear the chart shows where price is. the surface shows what people are paying to be wrong and the shape carries more than direction steepness tells you how much tail risk is priced term structure tells you whether the fear is about this week or this quarter curvature tells you how much the market disagrees with its own base case retail sees one implied vol number on the option they're about to buy a desk sees a surface and trades the difference between its shape and what that shape usually looks like every input is public. the chain lists IV at every strike and expiry strike on one axis, expiry on another, IV on the third. the surface builds itself black-scholes won a nobel for a formula the market has been visibly disagreeing with since 1987 the disagreement is the signal full breakdown in the article below

delost

89,999 görüntüleme • 1 ay önce

This is what it means to be on the charts for 20 years. For a trader, experience is not a detail, it is the real key to success. We are not talking about indicators, magic setups or current trends, but about eye, market memory and the ability to recognise the same dynamics that repeat themselves in different forms. This video found online explains it perfectly: it makes you understand how, over time, you stop reacting to candlesticks and start reading the market's intentions. You know when there is noise, when there is distribution, when they are just looking for liquidity... and above all, you know when to do nothing. That's where the real advantage lies. The rest is just haste, ego and overtrading. Experience does not accelerate the market. It accelerates you.
0:42

This is what it means to be on the charts for 20 years. For a trader, experience is not a detail, it is the real key to success. We are not talking about indicators, magic setups or current trends, but about eye, market memory and the ability to recognise the same dynamics that repeat themselves in different forms. This video found online explains it perfectly: it makes you understand how, over time, you stop reacting to candlesticks and start reading the market's intentions. You know when there is noise, when there is distribution, when they are just looking for liquidity... and above all, you know when to do nothing. That's where the real advantage lies. The rest is just haste, ego and overtrading. Experience does not accelerate the market. It accelerates you.

EliZ

61,654 görüntüleme • 8 ay önce

🚨 S&P 500 IS WALKING STRAIGHT INTO A WYCKOFF TRAP This is one of the oldest market structure models in history. And right now the S&P 500 is playing it out perfectly Preliminary Supply. Buying Climax. Upthrust. Post-Distribution Every phase has printed exactly where the model predicted We are now entering the Weakness phase - and if the structure holds, the target sits at 4,180 The market just hit new all-time highs while oil is above $120 and geopolitical risk is at decade highs That's not strength. That's the last trap before the drop NOTIFS ON!
0:53

🚨 S&P 500 IS WALKING STRAIGHT INTO A WYCKOFF TRAP This is one of the oldest market structure models in history. And right now the S&P 500 is playing it out perfectly Preliminary Supply. Buying Climax. Upthrust. Post-Distribution Every phase has printed exactly where the model predicted We are now entering the Weakness phase - and if the structure holds, the target sits at 4,180 The market just hit new all-time highs while oil is above $120 and geopolitical risk is at decade highs That's not strength. That's the last trap before the drop NOTIFS ON!

NoName

135,802 görüntüleme • 3 ay önce

"Healthcare is human and the most human signal is a conversation. "That moment between the clinician and patient is where we can bend the trajectory on experiences, outcomes, and costs. "When we apply intelligence across different systems, and surface the right information at the right time with the right context at the moment of care, we can change everything." — Shiv Rao, MD
0:43

"Healthcare is human and the most human signal is a conversation. "That moment between the clinician and patient is where we can bend the trajectory on experiences, outcomes, and costs. "When we apply intelligence across different systems, and surface the right information at the right time with the right context at the moment of care, we can change everything." — Shiv Rao, MD

Abridge

156,920 görüntüleme • 2 ay önce

🚨 READ THIS CAREFULLY The S&P 500 is entering the most dangerous part of the pattern. We are currently at point 6. The final all-time high zone. This is the phase where everyone thinks the worst is over. It isn’t. This is where the market turns back. This is a 7 point structure. And so far, it has followed the script perfectly. 1 → First support touch. 2 → First rejection from upper resistance. 3 → Second support touch. 4 → Second rejection. 5 → Panic flush into lower support. 6 → Final all-time high and reversal zone. Now comes 7. That is not the start of the dip. That is already the target zone after the reversal. And this is where the real damage starts. If this structure keeps playing out, the S&P 500 is heading toward 5,200 to 5,300. That is where this pattern completes. We are not there yet. Narratives will break there. Bulls will give up there. And most people will only realize it when the dump is already deep. This is exactly why I keep saying the market is much weaker than it looks. The rally is not strength. It is the final squeeze before the next leg down. I’ve studied macro for 10 years and I called almost every major market top, including the October BTC ATH. Follow and turn notifications on. I’ll post the warning BEFORE it hits the headlines.
0:30

🚨 READ THIS CAREFULLY The S&P 500 is entering the most dangerous part of the pattern. We are currently at point 6. The final all-time high zone. This is the phase where everyone thinks the worst is over. It isn’t. This is where the market turns back. This is a 7 point structure. And so far, it has followed the script perfectly. 1 → First support touch. 2 → First rejection from upper resistance. 3 → Second support touch. 4 → Second rejection. 5 → Panic flush into lower support. 6 → Final all-time high and reversal zone. Now comes 7. That is not the start of the dip. That is already the target zone after the reversal. And this is where the real damage starts. If this structure keeps playing out, the S&P 500 is heading toward 5,200 to 5,300. That is where this pattern completes. We are not there yet. Narratives will break there. Bulls will give up there. And most people will only realize it when the dump is already deep. This is exactly why I keep saying the market is much weaker than it looks. The rally is not strength. It is the final squeeze before the next leg down. I’ve studied macro for 10 years and I called almost every major market top, including the October BTC ATH. Follow and turn notifications on. I’ll post the warning BEFORE it hits the headlines.

Wimar.X

57,995 görüntüleme • 2 ay önce

What String Theory Does That Classical Physics Doesn’t Classical Physics begins with a point particle. Its position is described by one function of time x = x(t) As time passes, that point traces a curve through Spacetime called a worldline. String theory changes the starting object. A string has length, so time alone is not enough. We also need a coordinate along the string Xᵘ = Xᵘ(τ, σ) Here τ is worldsheet time, and σ labels position along the string. As the string moves, it sweeps out a surface called a worldsheet. The action measures the area of that surface S = -T ∫ dτ dσ √[-det hₐᵦ] This is where string theory begins. #StringTheory #ClassicalMechanics #TheoreticalPhysics #Worldsheet #QuantumGravity #MathematicalPhysics #Spacetime #PhysicsVisualization #ScienceEducation
0:55

What String Theory Does That Classical Physics Doesn’t Classical Physics begins with a point particle. Its position is described by one function of time x = x(t) As time passes, that point traces a curve through Spacetime called a worldline. String theory changes the starting object. A string has length, so time alone is not enough. We also need a coordinate along the string Xᵘ = Xᵘ(τ, σ) Here τ is worldsheet time, and σ labels position along the string. As the string moves, it sweeps out a surface called a worldsheet. The action measures the area of that surface S = -T ∫ dτ dσ √[-det hₐᵦ] This is where string theory begins. #StringTheory #ClassicalMechanics #TheoreticalPhysics #Worldsheet #QuantumGravity #MathematicalPhysics #Spacetime #PhysicsVisualization #ScienceEducation

Mathelirium

18,616 görüntüleme • 1 ay önce

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask
1:27

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,315 görüntüleme • 2 ay önce

Bannon: Pam Bondi where are the prosecutions of the people that did the invasion that set up the invasion of the southern border? FBI Director Kash Patel where are the arrests? Are we going to get serious about this or are we not? Todd Bensman is on here all the time. He said it was intricately set up to mass invade the country. Where are those politicians? Where is Mayorkas? Where is the grand jury? Where are the investigations? It is one thing to provide a platform to hear that heart wrenching story. But how are you going to stop it in the future? It is take action now. The reason for the frustration of this audience after all the work and effort to get here is let’s do something. Do not think you are going to be here forever. You cannot plan on that. Action. Action. Action.
2:03

Bannon: Pam Bondi where are the prosecutions of the people that did the invasion that set up the invasion of the southern border? FBI Director Kash Patel where are the arrests? Are we going to get serious about this or are we not? Todd Bensman is on here all the time. He said it was intricately set up to mass invade the country. Where are those politicians? Where is Mayorkas? Where is the grand jury? Where are the investigations? It is one thing to provide a platform to hear that heart wrenching story. But how are you going to stop it in the future? It is take action now. The reason for the frustration of this audience after all the work and effort to get here is let’s do something. Do not think you are going to be here forever. You cannot plan on that. Action. Action. Action.

Grace Chong, MBI

187,421 görüntüleme • 1 yıl önce

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you
0:30

every time you hit buy, the market maker on the other side already solved for two numbers not on the asset - on you specifically the first: their internal fair value. not the midpoint you see on screen their number is adjusted for the last 30 seconds of order flow, their current inventory imbalance and which volatility regime the model thinks the market is in it's different from the displayed price, and they never show it to you the second: your adverse selection probability. a live estimate of whether you're informed or just emotional if the model flags you as a potential informed trader, the spread widens before your fill - automatically, invisibly, before you notice anything changed that bad fill you blamed on "low liquidity"? sometimes it's a market maker's algorithm deciding you look too dangerous to fill at the quoted price this is Glosten-Milgrom - published 1985, sits in every market microstructure textbook on earth, runs inside every serious market making operation that's ever quoted you a price Bookmark this feature before it lost when you click buy, you're answering a question you didn't know was being asked retail spends all its time thinking about where price is going the desk filling your order is spending that same time thinking about you

Livsun

18,906 görüntüleme • 1 ay önce

The S&P looks calm. The stocks inside it are anything but. Cem Karsan Cem Karsan 🥐 says that gap is the whole story right now. When index volatility gets compressed this hard, correlation between the individual names starts breaking down, and the index quietly masks how much those names are actually moving. That calm surface is not real. It is compression hiding what is underneath.
1:47

The S&P looks calm. The stocks inside it are anything but. Cem Karsan Cem Karsan 🥐 says that gap is the whole story right now. When index volatility gets compressed this hard, correlation between the individual names starts breaking down, and the index quietly masks how much those names are actually moving. That calm surface is not real. It is compression hiding what is underneath.

tastylive

39,770 görüntüleme • 2 gün önce

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
0:26

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

Livsun

188,928 görüntüleme • 3 ay önce

Do you actually know what convex optimization is in the geometric, guarantee-theoretic sense or have you only met it through solvers and loss curves? Convexity is rare comfort in optimization...there are no spurious local minima, no surprise traps, and inequalities you can use like tools instead of prayers. So, what is this convexity? Let x = (x₁, x₂) and let f(x) be convex. Plot the surface z = f(x). Pick a contact point x₀. The local slope is the gradient p = ∇f(x₀). That p is exactly the data that defines the supporting plane: z = f(x₀) + p · (x − x₀). Thus, f is said to be convex because for every x, f(x) ≥ f(x₀) + p · (x − x₀). So the plane at x₀ can slide under the surface, but it never slices through it. Not near the point...everywhere. Now for here is the interesting part: The slope becomes a coordinate system! Rewrite the same plane as z = p · x − b, where b is the offset. Because the plane passes through (x₀, f(x₀)), the offset is forced to be b = p · x₀ − f(x₀). And that number isn’t just geometry trivia. It’s the convex conjugate: f*(p) = sup over x ( p · x − f(x) ). At a differentiable contact point, the supporting plane touches f tightly enough that the supremum is achieved at x₀, giving the identity f*(p) = p · x₀ − f(x₀) when p = ∇f(x₀). So one moving contact point gives two linked readouts: primal position x₀ dual position (slope) p = ∇f(x₀) dual offset f*(p) One surface. Two worlds. #ConvexOptimization #Optimization #MachineLearning #SignalProcessing #AppliedMath #Engineering
0:50

Do you actually know what convex optimization is in the geometric, guarantee-theoretic sense or have you only met it through solvers and loss curves? Convexity is rare comfort in optimization...there are no spurious local minima, no surprise traps, and inequalities you can use like tools instead of prayers. So, what is this convexity? Let x = (x₁, x₂) and let f(x) be convex. Plot the surface z = f(x). Pick a contact point x₀. The local slope is the gradient p = ∇f(x₀). That p is exactly the data that defines the supporting plane: z = f(x₀) + p · (x − x₀). Thus, f is said to be convex because for every x, f(x) ≥ f(x₀) + p · (x − x₀). So the plane at x₀ can slide under the surface, but it never slices through it. Not near the point...everywhere. Now for here is the interesting part: The slope becomes a coordinate system! Rewrite the same plane as z = p · x − b, where b is the offset. Because the plane passes through (x₀, f(x₀)), the offset is forced to be b = p · x₀ − f(x₀). And that number isn’t just geometry trivia. It’s the convex conjugate: f*(p) = sup over x ( p · x − f(x) ). At a differentiable contact point, the supporting plane touches f tightly enough that the supremum is achieved at x₀, giving the identity f*(p) = p · x₀ − f(x₀) when p = ∇f(x₀). So one moving contact point gives two linked readouts: primal position x₀ dual position (slope) p = ∇f(x₀) dual offset f*(p) One surface. Two worlds. #ConvexOptimization #Optimization #MachineLearning #SignalProcessing #AppliedMath #Engineering

Mathelirium

38,506 görüntüleme • 7 ay önce

🚨 EVERY TIME INFLATION SPIKED, THE S&P 500 DUMPED... This is not a theory. This is simple pattern. Decades of data point in one direction: 2001: Inflation surged -> The S&P 500 collapsed 2008: Same story -> Different decade 2022: CPI spiked to 9% -> The S&P 500 dropped 25% Now look at where we are right now. CPI is rising again. PPI is already running hot. The market is pricing in a "NORMAL CORRECTION". But the data is screaming "HUGE COLLAPSE". Do NOT say I didn't warn you later...
0:38

🚨 EVERY TIME INFLATION SPIKED, THE S&P 500 DUMPED... This is not a theory. This is simple pattern. Decades of data point in one direction: 2001: Inflation surged -> The S&P 500 collapsed 2008: Same story -> Different decade 2022: CPI spiked to 9% -> The S&P 500 dropped 25% Now look at where we are right now. CPI is rising again. PPI is already running hot. The market is pricing in a "NORMAL CORRECTION". But the data is screaming "HUGE COLLAPSE". Do NOT say I didn't warn you later...

ᴛʀᴀᴄᴇʀ

67,526 görüntüleme • 2 ay önce

🔥🔥HUGH FIRES EVERYWHERE🔥 DERIVATIVES OVER LEVERAGE RISK🔥🔥💣💣🔥🔥 The system is at capacity IMO. When you see numbers in the high Trillions and Quadrillions and add huge amounts of leverage the system is not in good shape. Gold and Silver are not going up the system is failing. The system is backed by trust only. TRUST IS BROKEN. Suppressing the price of Precious Metals is an illusion it's not based on supply and demand it's based on how much paper can we throw at the Metals to hold them down. This is a pressure cooker MARKET MANIPULATION IN EVERY MARKET
4:51

🔥🔥HUGH FIRES EVERYWHERE🔥 DERIVATIVES OVER LEVERAGE RISK🔥🔥💣💣🔥🔥 The system is at capacity IMO. When you see numbers in the high Trillions and Quadrillions and add huge amounts of leverage the system is not in good shape. Gold and Silver are not going up the system is failing. The system is backed by trust only. TRUST IS BROKEN. Suppressing the price of Precious Metals is an illusion it's not based on supply and demand it's based on how much paper can we throw at the Metals to hold them down. This is a pressure cooker MARKET MANIPULATION IN EVERY MARKET

Stephanie 🇬🇧🇺🇸🦍

22,369 görüntüleme • 9 ay önce

"The Iranian regime is not a hierarchical system where decapitation is even “on the table”. So, even if you were to kill the Supreme Leader — or even the line of succession — which is, I think, what the Israelis are trying at the moment by taking out operational and strategic commanders across the IRGC, theocratic leaders, and political leaders, it doesn’t mean the regime is just going to collapse. The fact is, what we’ve seen since January is a complete decentralisation of the chain of command and command-and-control. So, despite this heavy attack this morning, there is no paralysis in Iran. Iran is sustaining it. They were very quick to strike back across the Gulf — escalating — and the operational tempo is still there. That shows command-and-control has been delegated down to the local level. We also see, for example, that the Basij — the major component of the IRGC that controls the public on the ground — has been on alert for quite some time and has organised itself at the local level without having to rely constantly on the main chain of command in Tehran"
6:00

"The Iranian regime is not a hierarchical system where decapitation is even “on the table”. So, even if you were to kill the Supreme Leader — or even the line of succession — which is, I think, what the Israelis are trying at the moment by taking out operational and strategic commanders across the IRGC, theocratic leaders, and political leaders, it doesn’t mean the regime is just going to collapse. The fact is, what we’ve seen since January is a complete decentralisation of the chain of command and command-and-control. So, despite this heavy attack this morning, there is no paralysis in Iran. Iran is sustaining it. They were very quick to strike back across the Gulf — escalating — and the operational tempo is still there. That shows command-and-control has been delegated down to the local level. We also see, for example, that the Basij — the major component of the IRGC that controls the public on the ground — has been on alert for quite some time and has organised itself at the local level without having to rely constantly on the main chain of command in Tehran"

Dr Andreas Krieg

224,057 görüntüleme • 5 ay önce

NASA DID NOT TRUST ONE SENSOR that is why Apollo landed. radar was noisy. gyros drifted. the computer had to guess where the spacecraft really was while every instrument lied a little. that same problem now sits on quant desks. not “where is the spacecraft?” but: what is the real relationship between these two assets right now? most retail traders freeze that relationship into one number. run regression get beta trade the spread pretend the ratio stays alive it does not. the market keeps moving. the hedge ratio drifts. your “market neutral” position quietly becomes directional. a Kalman filter estimates a hidden state from noisy measurements. it does not blindly trust the last tick. it does not blindly trust the old model. it blends both. every tick becomes: > predict the current state > observe the noisy price > measure the surprise > decide how much to trust it > update the estimate that trust-weight is the whole trick. if the old estimate is shaky and the new data is clean, the filter moves fast. if the old estimate is strong and the tick is noisy, it barely moves. that is why a static beta is a dead photograph. a Kalman hedge ratio is a live estimate. the same math that helped NASA fuse noisy signals now helps quants keep a pairs trade from lying to them.
1:47

NASA DID NOT TRUST ONE SENSOR that is why Apollo landed. radar was noisy. gyros drifted. the computer had to guess where the spacecraft really was while every instrument lied a little. that same problem now sits on quant desks. not “where is the spacecraft?” but: what is the real relationship between these two assets right now? most retail traders freeze that relationship into one number. run regression get beta trade the spread pretend the ratio stays alive it does not. the market keeps moving. the hedge ratio drifts. your “market neutral” position quietly becomes directional. a Kalman filter estimates a hidden state from noisy measurements. it does not blindly trust the last tick. it does not blindly trust the old model. it blends both. every tick becomes: > predict the current state > observe the noisy price > measure the surprise > decide how much to trust it > update the estimate that trust-weight is the whole trick. if the old estimate is shaky and the new data is clean, the filter moves fast. if the old estimate is strong and the tick is noisy, it barely moves. that is why a static beta is a dead photograph. a Kalman hedge ratio is a live estimate. the same math that helped NASA fuse noisy signals now helps quants keep a pairs trade from lying to them.

Myttle

20,673 görüntüleme • 1 ay önce

The world Paperweight Title. One reason I curate so much is to know where we came from and where we are going. It is not always to agree or disagree with the past but to show it through the lens of that epoch. This is the only way to understand the past.
0:51

The world Paperweight Title. One reason I curate so much is to know where we came from and where we are going. It is not always to agree or disagree with the past but to show it through the lens of that epoch. This is the only way to understand the past.

Brian Roemmele

14,736 görüntüleme • 7 gün önce