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Quant from CERN built a model that catches market shocks when everyone panics, it profits the most But when to enter is a separate problem. Math already solved it: R = N/e ≈ 0.37 × N* > skip the first 37% of entry points > then take the first...

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sopersone

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121,533 просмотров • 6 месяцев назад •via X (Twitter)

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Ex-CERN QUANT applied quantum mechanics to the oil market on Polymarket He trades exclusively oil and made $226K in a week His Harmonic Oscillators model sees structure in the chaos: â†|n⟩ = √(n+1)|n+1⟩ -- growth â|n⟩ = √n|n-1⟩ -- decline Eₙ = ℏω(n + ½) -- energy level N̂|n⟩ = n|n⟩ -- particle number Price discretization: n(t) = ⌊(Rₜ - R_min)/ΔR · (N_L - 1)⌋ Rₜ = Σ r̃_τ/σ Market events: âₜ† = 1_{Δnₜ>0} · |Δnₜ| → buy âₜ = 1_{Δnₜ<0} · |Δnₜ| → sell Result: probability density of the next move: P(n,t) = 1/W · Σ δₙ,ₙτ Oil volatility for everyone is risk, for him it's a signal Check his profile:
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Ex-CERN QUANT applied quantum mechanics to the oil market on Polymarket He trades exclusively oil and made $226K in a week His Harmonic Oscillators model sees structure in the chaos: â†|n⟩ = √(n+1)|n+1⟩ -- growth â|n⟩ = √n|n-1⟩ -- decline Eₙ = ℏω(n + ½) -- energy level N̂|n⟩ = n|n⟩ -- particle number Price discretization: n(t) = ⌊(Rₜ - R_min)/ΔR · (N_L - 1)⌋ Rₜ = Σ r̃_τ/σ Market events: âₜ† = 1_{Δnₜ>0} · |Δnₜ| → buy âₜ = 1_{Δnₜ<0} · |Δnₜ| → sell Result: probability density of the next move: P(n,t) = 1/W · Σ δₙ,ₙτ Oil volatility for everyone is risk, for him it's a signal Check his profile:

sopersone

65,108 просмотров • 6 месяцев назад

Model 1 explained directly by the author, Romeo. Get it from the source, then watch the entry model video for a deeper breakdown. Study 📚
3:31

Model 1 explained directly by the author, Romeo. Get it from the source, then watch the entry model video for a deeper breakdown. Study 📚

Brighto

37,979 просмотров • 6 месяцев назад

THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.
0:46

THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.

sopersone

33,764 просмотров • 1 месяц назад

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?
1:01

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

Herman Trading

20,079 просмотров • 5 месяцев назад

Planetary VC founder Ramez Naam reveals that throwing 100 Claude Opus 5.5 agents at a problem was no better than using just 10, exposing a major limitation of AI swarms: "Test time compute scales worse than pre-training. Agents scale worse than test time compute. On the Opus 5.5 model card, it is like the worst scaling we've ever seen of anything." "The success of one agent was like 70%. It gained three points when you went to four agents, and then it gained two points when you went from four agents to 16. The Opus 5.5 model just topped out after 10 agents. It didn't get any better with 100 agents." "If you took the population of Columbus, Ohio, and said, 'Create general relativity,' would they do as well as Einstein on his own? There are many categories of problems where the peak intelligence of one entity is much more important than having a lot of entities at a lower level collaborating."
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Planetary VC founder Ramez Naam reveals that throwing 100 Claude Opus 5.5 agents at a problem was no better than using just 10, exposing a major limitation of AI swarms: "Test time compute scales worse than pre-training. Agents scale worse than test time compute. On the Opus 5.5 model card, it is like the worst scaling we've ever seen of anything." "The success of one agent was like 70%. It gained three points when you went to four agents, and then it gained two points when you went from four agents to 16. The Opus 5.5 model just topped out after 10 agents. It didn't get any better with 100 agents." "If you took the population of Columbus, Ohio, and said, 'Create general relativity,' would they do as well as Einstein on his own? There are many categories of problems where the peak intelligence of one entity is much more important than having a lot of entities at a lower level collaborating."

MTS

33,984 просмотров • 23 дней назад

I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.
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I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.

sopersone

12,515 просмотров • 1 месяц назад

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

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

Veles

2,191,157 просмотров • 3 месяцев назад

This 3-hour algorithmic trading video with Python is like getting a mini course from Massachusetts Institute of Technology - for free It reveals more about quant trading and bots than most traders learn in years on the market Bookmark it. Watch it. Then watch it again.
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This 3-hour algorithmic trading video with Python is like getting a mini course from Massachusetts Institute of Technology - for free It reveals more about quant trading and bots than most traders learn in years on the market Bookmark it. Watch it. Then watch it again.

sopersone

161,462 просмотров • 7 месяцев назад

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.
12:28

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.

R1X TRADES (GER40)

28,674 просмотров • 6 месяцев назад

a quant researcher at IMC (one of the oldest electronic market makers in the world) on a result that catches most candidates out in interviews. first, why "just predict further out, there's less noise" is wrong: "a number of people suggest they would try and predict one day into the future because there's less noise. i think they're thinking of some kind of central limit theorem idea." "but if you just think about what happens in markets ,the longer you look into the future, the more can happen. the more people can come and trade with you, the more idiosyncratic events." "looking further into the future has more variance and is harder to predict." then the counterintuitive part: "models trained for shorter term predictions can actually be better at predicting longer horizons than a model specifically trained on that target horizon." "you might train two models, one at 5 minutes, one at 10 minutes. and you might find the five minute model is actually better at predicting 10 minutes than the 10-minute model." why: "there's a battle between the signal you're trying to use getting diffused by noise the longer you look. it can be better to train on a shorter horizon which captures more of that short-term signal, and then leverage the conditional relationship between the 5 and 10-minute targets."
4:56

a quant researcher at IMC (one of the oldest electronic market makers in the world) on a result that catches most candidates out in interviews. first, why "just predict further out, there's less noise" is wrong: "a number of people suggest they would try and predict one day into the future because there's less noise. i think they're thinking of some kind of central limit theorem idea." "but if you just think about what happens in markets ,the longer you look into the future, the more can happen. the more people can come and trade with you, the more idiosyncratic events." "looking further into the future has more variance and is harder to predict." then the counterintuitive part: "models trained for shorter term predictions can actually be better at predicting longer horizons than a model specifically trained on that target horizon." "you might train two models, one at 5 minutes, one at 10 minutes. and you might find the five minute model is actually better at predicting 10 minutes than the 10-minute model." why: "there's a battle between the signal you're trying to use getting diffused by noise the longer you look. it can be better to train on a shorter horizon which captures more of that short-term signal, and then leverage the conditional relationship between the 5 and 10-minute targets."

Goshawk Trades

31,523 просмотров • 2 месяцев назад

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window
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THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 просмотров • 5 месяцев назад

A small group of quants wrote one formula, launched it on Polymarket and quietly took $20m in a year Their secret fits in one equation: A = e^(-λ₁T - λ₂y - λ₃R) · (1 + γ₀y · 1_{y>0.7}) · e^(-φ₀R²) · sgn(σₕ - κC)(1-C) These aren't random symbols each factor is a filter: > how stale the position is over time > how overheated the market is by returns > how much volatility justifies the entry > which direction to look at all Only if all four factors align the trade exists No "I feel like it". No "I believe in this asset" Just a number positive or not But there is one tool that changes the game completely Top traders on Polymarket are active simultaneously across multiple high-probability markets every day Each trade brings a small win on its own But combining 5-6 of these trades into one parlay you can increase your return from 80% to 400% ROI instead of five small wins That's exactly what this bot does automatically Try here:
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A small group of quants wrote one formula, launched it on Polymarket and quietly took $20m in a year Their secret fits in one equation: A = e^(-λ₁T - λ₂y - λ₃R) · (1 + γ₀y · 1_{y>0.7}) · e^(-φ₀R²) · sgn(σₕ - κC)(1-C) These aren't random symbols each factor is a filter: > how stale the position is over time > how overheated the market is by returns > how much volatility justifies the entry > which direction to look at all Only if all four factors align the trade exists No "I feel like it". No "I believe in this asset" Just a number positive or not But there is one tool that changes the game completely Top traders on Polymarket are active simultaneously across multiple high-probability markets every day Each trade brings a small win on its own But combining 5-6 of these trades into one parlay you can increase your return from 80% to 400% ROI instead of five small wins That's exactly what this bot does automatically Try here:

sopersone

57,574 просмотров • 6 месяцев назад

If you're harping on any of your fellow fans for not liking that performance, take it from the captain. If you didn't like it, you're right to not like it, and it is definitely a good thing that the guys in the room didn't like it themselves. Two points is two points, but they're better than that game.
0:44

If you're harping on any of your fellow fans for not liking that performance, take it from the captain. If you didn't like it, you're right to not like it, and it is definitely a good thing that the guys in the room didn't like it themselves. Two points is two points, but they're better than that game.

Matt Drake

27,463 просмотров • 6 месяцев назад

I GAVE MY GROK BOT $37 AND ONE JOB: READ THE ORDER BOOK BEFORE EVERY TRADE $37 → $8,423. 16 hours. The 25x trade made the least money. Most bots stare at the chart. Mine checks who's actually standing in the book before it touches anything, and if nobody's there, it doesn't trade. Grok Bot built the terminal for perps on BTC, ETH, SOL, AVAX and SUI. It ran through the history first, and now it decides on its own when an entry is worth it and when the book is too thin to sit in. - Measures real depth on both sides before sizing - Never enters bigger than it can exit without slipage - Sits on the bid instead of hitting market - Won't drag a position into a big sell wall - Margin stays at 37-59% of the deposit, the rest sits free That last rule is what kept $37 alive early on. Small accounts usually go all in on the first trade and pray. Mine left a chunk free every single time. The five big closes made $4,560, and the ranking surprised me. SUI at 10x made the most, $1,440. BTC at 25x made the least, $409. Leverage wasn't the edge, the book was. It's also slow. There were long stretches where it did nothing because the book was too thin, and I almost restarted it thinking it had frozen. My favorite moment of the run: a fat sell wall parked right above price. The bot saw it, stopped, and just refused to push into it. The other $3,826 is a stream of small trades in between. Thats almost half the result, all routine, and it's already net of 3.5% fees on every trade. 25x on BTC and 13x on ETH are my desk settings, not a signal. Don't copy the sizing. Hour 16. $8,423 on the screen, and the bot sitting on the bid again, waiting for someone to show up on the other side.
0:30

I GAVE MY GROK BOT $37 AND ONE JOB: READ THE ORDER BOOK BEFORE EVERY TRADE $37 → $8,423. 16 hours. The 25x trade made the least money. Most bots stare at the chart. Mine checks who's actually standing in the book before it touches anything, and if nobody's there, it doesn't trade. Grok Bot built the terminal for perps on BTC, ETH, SOL, AVAX and SUI. It ran through the history first, and now it decides on its own when an entry is worth it and when the book is too thin to sit in. - Measures real depth on both sides before sizing - Never enters bigger than it can exit without slipage - Sits on the bid instead of hitting market - Won't drag a position into a big sell wall - Margin stays at 37-59% of the deposit, the rest sits free That last rule is what kept $37 alive early on. Small accounts usually go all in on the first trade and pray. Mine left a chunk free every single time. The five big closes made $4,560, and the ranking surprised me. SUI at 10x made the most, $1,440. BTC at 25x made the least, $409. Leverage wasn't the edge, the book was. It's also slow. There were long stretches where it did nothing because the book was too thin, and I almost restarted it thinking it had frozen. My favorite moment of the run: a fat sell wall parked right above price. The bot saw it, stopped, and just refused to push into it. The other $3,826 is a stream of small trades in between. Thats almost half the result, all routine, and it's already net of 3.5% fees on every trade. 25x on BTC and 13x on ETH are my desk settings, not a signal. Don't copy the sizing. Hour 16. $8,423 on the screen, and the bot sitting on the bid again, waiting for someone to show up on the other side.

tenzo

129,733 просмотров • 28 дней назад

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.
29:44

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 просмотров • 4 месяцев назад

Years ago, ICT dropped this 37-second clip that broke the internet☄️ 37 Seconds is all ICT needed to cover: • 2022 Model • The Silver Bullet • Market Maker Sell Model, MMSM • Market Structure Shift, MSS • TIME > PRICE 37 SECONDS. THE AMOUNT OF INFORMATION HERE IS INSANE!
0:36

Years ago, ICT dropped this 37-second clip that broke the internet☄️ 37 Seconds is all ICT needed to cover: • 2022 Model • The Silver Bullet • Market Maker Sell Model, MMSM • Market Structure Shift, MSS • TIME > PRICE 37 SECONDS. THE AMOUNT OF INFORMATION HERE IS INSANE!

JeRRy MMXM

77,911 просмотров • 1 месяц назад

Successfully converted the Fractal Model logic to Python. Built a feature where you can enter any date and pull up that day’s chart. Why does this matter? Once this runs as a backtest/bot, it generates a trade report. Say the report shows a trade on Aug 21, 2018. You can enter that date and visually verify exactly where the entry and exit were. This is a few steps beyond anything you’ve seen from these backtests before. Normally everyone only posts numbers, but there's no way to visually double check reports or images. Expecting to finish in a few days, maybe a week. I take my time verifying before I claim any stats or results.
0:20

Successfully converted the Fractal Model logic to Python. Built a feature where you can enter any date and pull up that day’s chart. Why does this matter? Once this runs as a backtest/bot, it generates a trade report. Say the report shows a trade on Aug 21, 2018. You can enter that date and visually verify exactly where the entry and exit were. This is a few steps beyond anything you’ve seen from these backtests before. Normally everyone only posts numbers, but there's no way to visually double check reports or images. Expecting to finish in a few days, maybe a week. I take my time verifying before I claim any stats or results.

Ken - CantoLab

27,400 просмотров • 2 месяцев назад

This is still one of the best videos explaining the differences between Judaism and Christianity when it comes to Jesus. A lot of it is shocking at first, but it is true.
11:37

This is still one of the best videos explaining the differences between Judaism and Christianity when it comes to Jesus. A lot of it is shocking at first, but it is true.

Uncommon Sense

231,128 просмотров • 1 год назад

This guy built a Quant Resolution model and it made $2,191 a day on Polymarket The result is already +$363,748 PnL in 166 days with only $38 per average trade, repeating the same setup 10 times every hour across BTC “Up or Down” markets Several AI agents can split the workflow between probability, sizing and execution: 1. The model measures how far BTC is from the reference price A simplified version looks like: z = (S - K) / (σ√T) Where: S = current BTC price K = reference price σ = volatility T = time left That z-score is converted into the probability of BTC finishing Up or Down 2. It trades the gap between model probability and Polymarket Model Up = 64% Market Up = 54¢ Edge = 10¢ If the gap is large enough, the system enters As the market catches up to the model estimate, the edge disappears This trader’s Polymarket account: powerwinner Follow this account and trade on Polymarket through this bot: 41,268 predictions with a 51% win rate Most profitable trades: $683 → $3,420 (+$2,737) $396 → $3,040 (+$2,644) $531 → $3,040 (+$2,509) A setup like this can be coordinated with Grok Bot, while external feeds provide live BTC data and execution
0:25

This guy built a Quant Resolution model and it made $2,191 a day on Polymarket The result is already +$363,748 PnL in 166 days with only $38 per average trade, repeating the same setup 10 times every hour across BTC “Up or Down” markets Several AI agents can split the workflow between probability, sizing and execution: 1. The model measures how far BTC is from the reference price A simplified version looks like: z = (S - K) / (σ√T) Where: S = current BTC price K = reference price σ = volatility T = time left That z-score is converted into the probability of BTC finishing Up or Down 2. It trades the gap between model probability and Polymarket Model Up = 64% Market Up = 54¢ Edge = 10¢ If the gap is large enough, the system enters As the market catches up to the model estimate, the edge disappears This trader’s Polymarket account: powerwinner Follow this account and trade on Polymarket through this bot: 41,268 predictions with a 51% win rate Most profitable trades: $683 → $3,420 (+$2,737) $396 → $3,040 (+$2,644) $531 → $3,040 (+$2,509) A setup like this can be coordinated with Grok Bot, while external feeds provide live BTC data and execution

Daniro

11,565 просмотров • 1 месяц назад