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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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delost

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72,579 просмотров • 4 месяцев назад •via X (Twitter)

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Комментарии: 12

Фото профиля Shakee Trades
Shakee Trades4 месяцев назад

Who tf would make their model display in this pointless way 😒

Фото профиля beamnxw ./
beamnxw ./4 месяцев назад

u’r a quant guru

Фото профиля rari
rari4 месяцев назад

asking how much to hold beats forcing every model into buy or sell

Фото профиля Mark R Pommrehn
Mark R Pommrehn4 месяцев назад

Very cool!

Фото профиля Nahid
Nahid4 месяцев назад

the focus on velocity changes is super interesting

Фото профиля 捡瓶盖的
捡瓶盖的4 месяцев назад

怎么用

Фото профиля Harry Tandy
Harry Tandy4 месяцев назад

position sizing based on physics beats basic indicators anytime

Фото профиля アキラ
アキラ4 месяцев назад

Interesting concept, focusing on market momentum instead of direction.

Фото профиля rayyy
rayyy4 месяцев назад

Doubtful

Фото профиля Radar 24h
Radar 24h4 месяцев назад

Interesting concept, focusing on market momentum shift prediction.

Фото профиля Gipp 🦅
Gipp 🦅4 месяцев назад

These are the best formulas honestly

Фото профиля Aleiah
Aleiah4 месяцев назад

Math which brings money right?

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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
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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

Mathelirium

32,127 просмотров • 6 месяцев назад

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
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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 просмотров • 6 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
0:34

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 • 3 месяцев назад

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

30,529 просмотров • 7 месяцев назад

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 просмотров • 3 месяцев назад

A physical system, its phase-space state, and the energy function that governs the flow, all linked together. The point of the animation is that these are not three different objects. They are three views of the same dynamics. The pendulum on the left is the physical motion. The curve on the floor is the evolution of its state. The translucent surface shows the energy landscape that organizes that motion.
0:30

A physical system, its phase-space state, and the energy function that governs the flow, all linked together. The point of the animation is that these are not three different objects. They are three views of the same dynamics. The pendulum on the left is the physical motion. The curve on the floor is the evolution of its state. The translucent surface shows the energy landscape that organizes that motion.

Mathelirium

19,916 просмотров • 6 месяцев назад

The refining margin just told on the oil market. The standard model says a refiner turns three barrels of crude into two of gasoline and one of diesel. Run today's numbers and the implied gross margin blows past a hundred and eighty dollars across that barrel set. That is not efficiency. That is a warning. Crude collapsed fast. Finished fuel did not follow at the same speed. The two markets are recovering from the same shock at completely different paces, and the spread between them is the proof. Watch the spread, not the headline price of oil. The spread is telling you where the actual imbalance is sitting.
2:08

The refining margin just told on the oil market. The standard model says a refiner turns three barrels of crude into two of gasoline and one of diesel. Run today's numbers and the implied gross margin blows past a hundred and eighty dollars across that barrel set. That is not efficiency. That is a warning. Crude collapsed fast. Finished fuel did not follow at the same speed. The two markets are recovering from the same shock at completely different paces, and the spread between them is the proof. Watch the spread, not the headline price of oil. The spread is telling you where the actual imbalance is sitting.

Jeffrey P. Snider

22,579 просмотров • 2 месяцев назад

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

61,735 просмотров • 10 месяцев назад

Chasing a cruise missile mid-air. Most cruise missile travels at subsonic speeds, which is why the jet is easily able to keep up with it. The missile knows where it is at all times. It knows this because it knows where it isn't. By subtracting where it is from where it isn't, or where it isn't from where it is (whichever is greater), it obtains a difference, or deviation. The guidance subsystem uses deviations to generate corrective commands to drive the missile from a position where it is to a position where it isn't, and arriving at a position where it wasn't, it now is. Consequently, the position where it is, is now the position that it wasn't, and it follows that the position that it was, is now the position that it isn't. In the event that the position that it is in is not the position that it wasn't, the system has acquired a variation, the variation being the difference between where the missile is, and where it wasn't. If variation is considered to be a significant factor, it too may be corrected by the GEA. However, the missile must also know where it was. The missile guidance computer scenario works as follows. Because a variation has modified some of the information the missile has obtained, it is not sure just where it is. However, it is sure where it isn't, within reason, and it knows where it was. It now subtracts where it should be from where it wasn't, or vice-versa, and by differentiating this from the algebraic sum of where it shouldn't be, and where it was, it is able to obtain the deviation and its variation, which is called error.
0:32

Chasing a cruise missile mid-air. Most cruise missile travels at subsonic speeds, which is why the jet is easily able to keep up with it. The missile knows where it is at all times. It knows this because it knows where it isn't. By subtracting where it is from where it isn't, or where it isn't from where it is (whichever is greater), it obtains a difference, or deviation. The guidance subsystem uses deviations to generate corrective commands to drive the missile from a position where it is to a position where it isn't, and arriving at a position where it wasn't, it now is. Consequently, the position where it is, is now the position that it wasn't, and it follows that the position that it was, is now the position that it isn't. In the event that the position that it is in is not the position that it wasn't, the system has acquired a variation, the variation being the difference between where the missile is, and where it wasn't. If variation is considered to be a significant factor, it too may be corrected by the GEA. However, the missile must also know where it was. The missile guidance computer scenario works as follows. Because a variation has modified some of the information the missile has obtained, it is not sure just where it is. However, it is sure where it isn't, within reason, and it knows where it was. It now subtracts where it should be from where it wasn't, or vice-versa, and by differentiating this from the algebraic sum of where it shouldn't be, and where it was, it is able to obtain the deviation and its variation, which is called error.

UFO mania

88,594 просмотров • 21 дней назад

🚨 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

135,928 просмотров • 5 месяцев назад

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
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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 просмотров • 4 месяцев назад

🚨 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.
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🚨 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 просмотров • 4 месяцев назад

"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
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"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

156,989 просмотров • 4 месяцев назад

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

13,367 просмотров • 4 месяцев назад

This is a visualization of a Rydberg wavepacket. In simple terms it is a way of showing an electron in a very highly excited quantum state. The Math comes from the Schrödinger equation for hydrogen where the electron is described by a wavefunction rather than as a tiny ball moving on a fixed path. The yellow sphere at the centre is the proton, not the electron. The colored 3D surface around it shows places where the electron is more likely to be, i.e. a probability cloud.
0:56

This is a visualization of a Rydberg wavepacket. In simple terms it is a way of showing an electron in a very highly excited quantum state. The Math comes from the Schrödinger equation for hydrogen where the electron is described by a wavefunction rather than as a tiny ball moving on a fixed path. The yellow sphere at the centre is the proton, not the electron. The colored 3D surface around it shows places where the electron is more likely to be, i.e. a probability cloud.

Mathelirium

17,743 просмотров • 2 месяцев назад

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 просмотров • 4 месяцев назад

RWA is starting to look less like one market and more like several different markets sharing the same label. I spent some time going through CoinGecko RWA Charts, and the most interesting part wasn’t the headline market cap. It was the gap between where the value is concentrated and where tokenization is expanding. At the time of this recording, the dashboard showed roughly $8.38B in tokenized RWA market cap and $1.53B in 24H volume. Capital is still concentrated in commodities Commodities: $5.397B Stocks: $2.383B ETFs: $607M That means commodities represent roughly 64% of the tracked market, while stocks sit around 28% and ETFs around 7%. Tokenized equities may be getting a lot of attention, but the capital distribution tells a different story. Issuer market cap and product breadth are telling two different stories On the issuer side, Tether and Paxos account for roughly $4.62B combined, more than half of the market cap shown in the issuer chart. Then switch the same dashboard from market cap to token count. Picture changes completely. Reality: 572 tokens Ondo Finance: 447 Dinari: 248 Robinhood Europe: 192 xStocks: 141 This is where the data becomes useful. Market cap shows where capital is concentrated. Token count shows where product coverage is broadest. Volume shows where actual trading activity is taking place. Looking at only one of those metrics can give you a very incomplete picture of what is happening across tokenized assets. CoinGecko’s RWA Charts puts all three together and lets you break them down by asset type or issuer, then move through timeframes from 24H all the way to Max. For me, that makes it more useful as a discovery tool than simply another market-cap page. You can actually see which parts of the RWA market are carrying value, which issuers are expanding coverage, and where activity is shifting. Explore the RWA data yourself:
0:53

RWA is starting to look less like one market and more like several different markets sharing the same label. I spent some time going through CoinGecko RWA Charts, and the most interesting part wasn’t the headline market cap. It was the gap between where the value is concentrated and where tokenization is expanding. At the time of this recording, the dashboard showed roughly $8.38B in tokenized RWA market cap and $1.53B in 24H volume. Capital is still concentrated in commodities Commodities: $5.397B Stocks: $2.383B ETFs: $607M That means commodities represent roughly 64% of the tracked market, while stocks sit around 28% and ETFs around 7%. Tokenized equities may be getting a lot of attention, but the capital distribution tells a different story. Issuer market cap and product breadth are telling two different stories On the issuer side, Tether and Paxos account for roughly $4.62B combined, more than half of the market cap shown in the issuer chart. Then switch the same dashboard from market cap to token count. Picture changes completely. Reality: 572 tokens Ondo Finance: 447 Dinari: 248 Robinhood Europe: 192 xStocks: 141 This is where the data becomes useful. Market cap shows where capital is concentrated. Token count shows where product coverage is broadest. Volume shows where actual trading activity is taking place. Looking at only one of those metrics can give you a very incomplete picture of what is happening across tokenized assets. CoinGecko’s RWA Charts puts all three together and lets you break them down by asset type or issuer, then move through timeframes from 24H all the way to Max. For me, that makes it more useful as a discovery tool than simply another market-cap page. You can actually see which parts of the RWA market are carrying value, which issuers are expanding coverage, and where activity is shifting. Explore the RWA data yourself:

Arjantit

31,764 просмотров • 1 месяц назад

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

189,575 просмотров • 5 месяцев назад

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

44,430 просмотров • 2 месяцев назад

🚨 MONDAY WILL BREAK THE MARKET JPMorgan is reportedly set to sell $160 BILLION in U.S. stocks right after the market opens The scary part? EVERY TIME JPMorgan sells stocks, the S&P 500 drops 10–20% Not 2% Not 5% 10–20% This is not just another sell order This is one of the biggest banks on earth selling a huge amount of stock at the worst possible moment The market open That is when funds react Traders react Algos react Big desks react And once everyone sees the same signal at the same time, the move can spread fast First stocks Then risk assets Then crypto Then everything people thought was “safe” The scary part is that most investors are still acting like Monday is normal It is not A $160B sell flow does not need to crash the market by itself It only needs to trigger everyone else That is how panic starts Most people will understand this after it is already too late I will be watching this closely Follow me and turn notifications ON The next warning may come before the market reacts
0:37

🚨 MONDAY WILL BREAK THE MARKET JPMorgan is reportedly set to sell $160 BILLION in U.S. stocks right after the market opens The scary part? EVERY TIME JPMorgan sells stocks, the S&P 500 drops 10–20% Not 2% Not 5% 10–20% This is not just another sell order This is one of the biggest banks on earth selling a huge amount of stock at the worst possible moment The market open That is when funds react Traders react Algos react Big desks react And once everyone sees the same signal at the same time, the move can spread fast First stocks Then risk assets Then crypto Then everything people thought was “safe” The scary part is that most investors are still acting like Monday is normal It is not A $160B sell flow does not need to crash the market by itself It only needs to trigger everyone else That is how panic starts Most people will understand this after it is already too late I will be watching this closely Follow me and turn notifications ON The next warning may come before the market reacts

Leshka.eth ⛩

30,560 просмотров • 4 месяцев назад