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quant traders before: > Kelly Criterion (position sizing) > Black-Scholes-Merton (options pricing) > Sharpe / Sortino Ratio (risk-adjusted return) > CAPM (expected return) > GARCH models (volatility forecasting) > Markowitz Mean-Variance Optimization (portfolio allocation) > Monte Carlo simulations (risk scenarios) now: all of the above. one AI.

sopersone

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72,399 просмотров • 15 дней назад •via X (Twitter)

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this bot has a 92% win rate look at how this bot evaluates probability using Black-Scholes and uses the Kelly Criterion to determine position sizes

this bot has a 92% win rate look at how this bot evaluates probability using Black-Scholes and uses the Kelly Criterion to determine position sizes

sopersone

22,770 просмотров • 4 месяцев назад

$6 formulas is not a playbook. It's half a playbook But here's what's missing Four models any serious quant stack should also account for: 1. Random Walk Paths dS_t = μS_tdt + σS_tdW_t Geometric Brownian Motion - the backbone of price simulation Without modeling hundreds of possible scenarios building up over time, you're flying blind on tail risk 2. Monte Carlo Simulation P(A) ≈ N_hit / N_total Thousands of random samples to solve problems that closed-form math can't This is how you stress-test your Kelly sizing before real capital hits the market 3. Probability Distribution f(x) = (1/σ√2π) · e^(-½((x-μ)/σ)²) Results converge into a clear distribution of potential risk If you're not mapping your P&L outcomes to a distribution, you're guessing not trading 4. Quantitative Analysis / VaR VaR_α = inf{l ∈ ℝ : P(L > l) ≤ 1 − α} Predicting the future by simulating uncertainty and volatility Value at Risk shows you the worst-case loss at a given confidence level, the one number risk desks actually care about The original playbook gives you the edge. These four give you the guardrails Without guardrails, edges kill you faster than no edge at all Auto-copy trades from pros like this and ride their edge:$

6 formulas is not a playbook. It's half a playbook But here's what's missing Four models any serious quant stack should also account for: 1. Random Walk Paths dS_t = μS_tdt + σS_tdW_t Geometric Brownian Motion - the backbone of price simulation Without modeling hundreds of possible scenarios building up over time, you're flying blind on tail risk 2. Monte Carlo Simulation P(A) ≈ N_hit / N_total Thousands of random samples to solve problems that closed-form math can't This is how you stress-test your Kelly sizing before real capital hits the market 3. Probability Distribution f(x) = (1/σ√2π) · e^(-½((x-μ)/σ)²) Results converge into a clear distribution of potential risk If you're not mapping your P&L outcomes to a distribution, you're guessing not trading 4. Quantitative Analysis / VaR VaR_α = inf{l ∈ ℝ : P(L > l) ≤ 1 − α} Predicting the future by simulating uncertainty and volatility Value at Risk shows you the worst-case loss at a given confidence level, the one number risk desks actually care about The original playbook gives you the edge. These four give you the guardrails Without guardrails, edges kill you faster than no edge at all Auto-copy trades from pros like this and ride their edge:

sopersone

15,585 просмотров • 5 месяцев назад

Harry Markowitz, the Nobel laureate who invented modern portfolio theory: "Every fund from Bridgewater to Citadel runs on one equation I wrote as a 25-year-old grad student. Wall Street pays quants $500K to use it. It's free." the thread above teaches you to build a portfolio the real way, with the mathematics of capital allocation. every line of it traces back to one paper markowitz wrote in 1952. before him, "don't put all your eggs in one basket" was folklore. he turned it into algebra. he proved a portfolio's risk isn't the average of its parts, it's driven by how the parts move together, the covariance. combine assets that don't move in lockstep and you cut risk without giving up return. that is the closest thing to a free lunch in all of finance, and he wrote the exact equation for how much of it you get. that single insight, mean-variance optimization, is the engine under every serious fund on earth. renaissance, bridgewater, citadel, your pension, all of them size risk with markowitz's math. he published it in 1952, won the nobel in 1990, and it sits in every textbook and this free lecture. same story i keep telling: the math that runs the trillion-dollar machine has been public and free for seventy years. here is the part markowitz himself warned about. the equation is only as good as the numbers you feed it, your estimates of return and covariance. feed it garbage and the "optimal" portfolio it hands back is confidently, precisely wrong, and it detonates in the exact crisis it was built to survive. the optimizer is free. estimating the future honestly, and knowing when to distrust your own inputs, is the entire job.

Harry Markowitz, the Nobel laureate who invented modern portfolio theory: "Every fund from Bridgewater to Citadel runs on one equation I wrote as a 25-year-old grad student. Wall Street pays quants $500K to use it. It's free." the thread above teaches you to build a portfolio the real way, with the mathematics of capital allocation. every line of it traces back to one paper markowitz wrote in 1952. before him, "don't put all your eggs in one basket" was folklore. he turned it into algebra. he proved a portfolio's risk isn't the average of its parts, it's driven by how the parts move together, the covariance. combine assets that don't move in lockstep and you cut risk without giving up return. that is the closest thing to a free lunch in all of finance, and he wrote the exact equation for how much of it you get. that single insight, mean-variance optimization, is the engine under every serious fund on earth. renaissance, bridgewater, citadel, your pension, all of them size risk with markowitz's math. he published it in 1952, won the nobel in 1990, and it sits in every textbook and this free lecture. same story i keep telling: the math that runs the trillion-dollar machine has been public and free for seventy years. here is the part markowitz himself warned about. the equation is only as good as the numbers you feed it, your estimates of return and covariance. feed it garbage and the "optimal" portfolio it hands back is confidently, precisely wrong, and it detonates in the exact crisis it was built to survive. the optimizer is free. estimating the future honestly, and knowing when to distrust your own inputs, is the entire job.

Rossst.03

44,131 просмотров • 29 дней назад

One common reason why traders blow up is because of poor position sizing. In other words, how much do you bet on a trade. You can be right on direction 60% of the time and still lose everything if you size your positions poorly. One oversized trade can wipe out months of gains. This is why position sizing is a big part of risk management. Sandeep Rao - SEBI Reg. RA🖖 recently spoke to Tom Basso, one of the original Market Wizards, to discuss his approach to trading. I was listening to the interview and the one thing that stood out to me was how Tom's thinking on position sizing evolved over decades. He started simple: risk the same percentage of equity on every trade, inspired by Larry Hite's philosophy that every bet should be equal in terms of potential loss. But then came a silver trade with explosive volatility. Clients were calling, nervous about the wild swings. So he realized it wasn't just about the amount you could lose—it was also about the speed of movement. High volatility creates psychological stress that leads to poor decisions. So he added a second layer: volatility as a percentage of equity. Now he'd calculate both risk % and volatility %, then take the smaller of the two. Then came the third refinement: margin-to-equity ratios. Some markets have deceptively low risk and volatility but require high margin because of sudden jump risk. By incorporating all three factors, he never got caught overexposed. The result was a position sizing system that automatically scales down when markets get too volatile, protects against margin squeezes, and keeps portfolio risk in check. It's really interesting conversation. Link to the full interview is in the comments.

One common reason why traders blow up is because of poor position sizing. In other words, how much do you bet on a trade. You can be right on direction 60% of the time and still lose everything if you size your positions poorly. One oversized trade can wipe out months of gains. This is why position sizing is a big part of risk management. Sandeep Rao - SEBI Reg. RA🖖 recently spoke to Tom Basso, one of the original Market Wizards, to discuss his approach to trading. I was listening to the interview and the one thing that stood out to me was how Tom's thinking on position sizing evolved over decades. He started simple: risk the same percentage of equity on every trade, inspired by Larry Hite's philosophy that every bet should be equal in terms of potential loss. But then came a silver trade with explosive volatility. Clients were calling, nervous about the wild swings. So he realized it wasn't just about the amount you could lose—it was also about the speed of movement. High volatility creates psychological stress that leads to poor decisions. So he added a second layer: volatility as a percentage of equity. Now he'd calculate both risk % and volatility %, then take the smaller of the two. Then came the third refinement: margin-to-equity ratios. Some markets have deceptively low risk and volatility but require high margin because of sudden jump risk. By incorporating all three factors, he never got caught overexposed. The result was a position sizing system that automatically scales down when markets get too volatile, protects against margin squeezes, and keeps portfolio risk in check. It's really interesting conversation. Link to the full interview is in the comments.

Nithin Kamath

59,268 просмотров • 7 месяцев назад

The best Polymarket Quant bot for copy trading with a 99.3% win rate The bot doesn't take risks it uses math and statistics to consistently achieve a 99% win probability. Using this approach, it has earned +$805,000 across 29,184 predictions with 99.3% accuracy Here's a breakdown of its algorithm: 1. Price Mispricing Detection δ = actual win probability − implied probability Traders constantly overpay for cheap contracts (0.1–50¢), but the main edge is concentrated in contracts (80–99¢) that's exactly where the bot trades 2. Expected Value (EV) Calculation EV = (P_win × Payout) − (P_loss × Cost) Shows whether a bet is worth placing regardless of the outcome of any single trade 3. Kelly Criterion f* = (p × b − q) / b The most powerful position sizing formula ever discovered for trading and prediction markets Tells the algorithm what percentage of your portfolio to put into each bet to win in the long run His profile: Those who copy traded him have already made $35,000 But those who followed him and used Poly_Parlay made over $100,000 How? By simply mirroring his trades with a multiplier Instead of opening single trades, they added them all to a basket, so the odds multiplied Bot access:

sopersone

15,755 просмотров • 3 месяцев назад

ex-Citadel portfolio manager, just explained why almost every fund actually blows up. "90% of hedge fund failures, PM failures, analyst failures, it's bad portfolio construction, sizing, all those things." the logic: alpha is competitive. one good idea barely moves the needle, because the moment you build a book, correlation starts eating you. "any amount of correlation in a portfolio drags your sharpe ratio toward the market's." and the market's sharpe you can buy for free from Vanguard. so the entire game collapses to two things: generate a positive hit rate across enough independent bets, and kill the correlation between them. the funds that survive run 80-90% of their risk in idiosyncratic, stock-specific bets. only 10-20% is allowed to be factor exposure. and they treat every factor bet, momentum, crowding, "long the AI trade", like a single stock position, with the same hard limits and the same diligence. the ones who blow up almost always had the ideas. they just leaked P&L on risk they never meant to take. "what they got wrong was risk and portfolio construction." clip from "Odds on Open"

ex-Citadel portfolio manager, just explained why almost every fund actually blows up. "90% of hedge fund failures, PM failures, analyst failures, it's bad portfolio construction, sizing, all those things." the logic: alpha is competitive. one good idea barely moves the needle, because the moment you build a book, correlation starts eating you. "any amount of correlation in a portfolio drags your sharpe ratio toward the market's." and the market's sharpe you can buy for free from Vanguard. so the entire game collapses to two things: generate a positive hit rate across enough independent bets, and kill the correlation between them. the funds that survive run 80-90% of their risk in idiosyncratic, stock-specific bets. only 10-20% is allowed to be factor exposure. and they treat every factor bet, momentum, crowding, "long the AI trade", like a single stock position, with the same hard limits and the same diligence. the ones who blow up almost always had the ideas. they just leaked P&L on risk they never meant to take. "what they got wrong was risk and portfolio construction." clip from "Odds on Open"

Goshawk Trades

39,360 просмотров • 1 месяц назад

Slow down. Read what real traders are saying: Our student results speak for themselves — consistent growth, clarity, and full independence. It’s not your strategy — it’s your ability to align the market environment and volatility condition and turn the right system, measured move and risk profile on. You’ll learn how to: • Know when to engage • Size correctly for volatility • Match risk profile to measured move expectancy • Keep your account alive long enough to compound 3 months for $100 includes: • OHLC Probability Engine – Intraday candle outcome modeling • HOD/LOD Scenario Tracker – Stat-based market context overlays • Time-Range Sequencer – Real-time session and volatility structuring • Monte Carlo Risk Suite – Trade modeling and position sizing engine • Decision Journal System – Trade review and probabilistic scoring tools We don’t cherry-pick trades or showcase payouts from one of hundreds of hidden prop accounts. We trade live — show all account, all execution, every day. Wins and losses. No magic. Just process. Our goal is to give you a complete, independent foundation — so by the end of three months, you no longer rely on anyone but your process, your decision making and your risk management. Sign up at Austin Clark Trades Dont Lie

The Daily Profiler

17,803 просмотров • 8 месяцев назад

Showed a friend these 50 quant formulas He dropped them into Claude and built a risk calculator for his portfolio in one prompt His first discovery: his 'profitable' strategy was actually bleeding 12% a year to variance drag and he never knew He was up 30% one month, down 30% the next, thought he broke even Actual return: -9% Two years of this. Compounding against him every single day He recalculated his entire portfolio in 10 minutes using code from the article. Found out his real Sharpe ratio was 0.3, not the 1.2 he assumed Then he ran the drawdown analysis. His max drawdown was -58% He thought it was -30% Every number he believed about his own performance was wrong These 50 concepts aren't theory. They're the exact toolkit quant desks use to see what retail never sees Full breakdown with runnable Python code below

Showed a friend these 50 quant formulas He dropped them into Claude and built a risk calculator for his portfolio in one prompt His first discovery: his 'profitable' strategy was actually bleeding 12% a year to variance drag and he never knew He was up 30% one month, down 30% the next, thought he broke even Actual return: -9% Two years of this. Compounding against him every single day He recalculated his entire portfolio in 10 minutes using code from the article. Found out his real Sharpe ratio was 0.3, not the 1.2 he assumed Then he ran the drawdown analysis. His max drawdown was -58% He thought it was -30% Every number he believed about his own performance was wrong These 50 concepts aren't theory. They're the exact toolkit quant desks use to see what retail never sees Full breakdown with runnable Python code below

Phosphen

129,374 просмотров • 5 месяцев назад

William Sharpe, Stanford professor and Nobel economist: "Citadel and Two Sigma live and die by one number: the Sharpe ratio. It's mine. I built it to tell a skilled strategy from a lucky, reckless one." the thread above is about building a portfolio and knowing when to cut a strategy. sharpe gave the world the single number that answers the only question that matters: is this thing actually good, or just lucky and levered? return alone is a trap. a 40% year isn't impressive if you bet the farm for it. sharpe's ratio divides your return by the risk you took to get it, so a calm 15% can beat a wild 40%. it strips luck and leverage out of any track record, and that is exactly why every fund on earth judges itself by it. this is also how a desk decides when to kill a strategy. not "did it make money," but "did it make enough per unit of risk." a falling sharpe is the signal to cut, long before the P&L ever turns red. he published the math in 1964, won the nobel in 1990, and left it in this free lecture. same story i keep telling: the number that runs every trading floor is old, public, and free. here is the blind spot sharpe himself knew. the ratio assumes risk is smooth and symmetric. a strategy that quietly sells insurance shows a gorgeous sharpe for years, then one tail event erases a decade. a high sharpe is not proof you are safe. it is proof you have not met your bad day yet.

William Sharpe, Stanford professor and Nobel economist: "Citadel and Two Sigma live and die by one number: the Sharpe ratio. It's mine. I built it to tell a skilled strategy from a lucky, reckless one." the thread above is about building a portfolio and knowing when to cut a strategy. sharpe gave the world the single number that answers the only question that matters: is this thing actually good, or just lucky and levered? return alone is a trap. a 40% year isn't impressive if you bet the farm for it. sharpe's ratio divides your return by the risk you took to get it, so a calm 15% can beat a wild 40%. it strips luck and leverage out of any track record, and that is exactly why every fund on earth judges itself by it. this is also how a desk decides when to kill a strategy. not "did it make money," but "did it make enough per unit of risk." a falling sharpe is the signal to cut, long before the P&L ever turns red. he published the math in 1964, won the nobel in 1990, and left it in this free lecture. same story i keep telling: the number that runs every trading floor is old, public, and free. here is the blind spot sharpe himself knew. the ratio assumes risk is smooth and symmetric. a strategy that quietly sells insurance shows a gorgeous sharpe for years, then one tail event erases a decade. a high sharpe is not proof you are safe. it is proof you have not met your bad day yet.

Voltex

65,224 просмотров • 29 дней назад

Quant at Jane Street kills 97 out of every 100 strategies it builds. That body count is the edge. Not the 3 survivors. The 97 corpses. The faster you falsify garbage, the faster you reach the one idea that isn't. Retail runs this backwards - finds one setup, gets emotionally married to it, and rides it down 40% over six months defending it like a religion. Run the kill-loop yourself with Horizon -> Type an idea in plain English. Horizon parses it into entry/exit logic, position sizing, risk rules. Backtests 5 years of tick data in ~12 seconds. Runs Monte Carlo across thousands of simulated paths. Deflates Sharpe ratio for the number of trials so luck can't sneak through. Spits out a cold verdict: dead or alive. Kill it. Type the next. 50 ideas before dinner. That loop cost $25K/year and a quant desk. Now it's a sentence and 12 seconds. The edge was never having ideas. It was murdering them at scale. Save this. Test 50. Keep 2. Kill the rest without mercy

Quant at Jane Street kills 97 out of every 100 strategies it builds. That body count is the edge. Not the 3 survivors. The 97 corpses. The faster you falsify garbage, the faster you reach the one idea that isn't. Retail runs this backwards - finds one setup, gets emotionally married to it, and rides it down 40% over six months defending it like a religion. Run the kill-loop yourself with Horizon -> Type an idea in plain English. Horizon parses it into entry/exit logic, position sizing, risk rules. Backtests 5 years of tick data in ~12 seconds. Runs Monte Carlo across thousands of simulated paths. Deflates Sharpe ratio for the number of trials so luck can't sneak through. Spits out a cold verdict: dead or alive. Kill it. Type the next. 50 ideas before dinner. That loop cost $25K/year and a quant desk. Now it's a sentence and 12 seconds. The edge was never having ideas. It was murdering them at scale. Save this. Test 50. Keep 2. Kill the rest without mercy

cvxv666

81,130 просмотров • 2 месяцев назад

my quant friend explained how polymarket AI bots actually work btc/eth/sol/xrp 5-min markets now live they use black-scholes for binary options C_binary = e^(−rT) · N(d₂) for 5-min contracts: model says 50% probability (coin flip) but bots print hundreds of thousands how: binance confirms 0.15% move at minute 3 true probability jumps to 92% polymarket still shows 65% bot captures 27% edge before odds update the pipeline: ▸ deribit implied volatility ▸ monte carlo 200k simulations ▸ detect 3%+ model divergence ▸ execute on lag they're not predicting direction they're arbitraging information delay polymarket launching 1-min markets signal-to-noise drops 2.24x only sub-100ms systems survive when fair value updates in milliseconds who controls the orderbook? forget about losing with Poly Bot Copy ⭣

my quant friend explained how polymarket AI bots actually work btc/eth/sol/xrp 5-min markets now live they use black-scholes for binary options C_binary = e^(−rT) · N(d₂) for 5-min contracts: model says 50% probability (coin flip) but bots print hundreds of thousands how: binance confirms 0.15% move at minute 3 true probability jumps to 92% polymarket still shows 65% bot captures 27% edge before odds update the pipeline: ▸ deribit implied volatility ▸ monte carlo 200k simulations ▸ detect 3%+ model divergence ▸ execute on lag they're not predicting direction they're arbitraging information delay polymarket launching 1-min markets signal-to-noise drops 2.24x only sub-100ms systems survive when fair value updates in milliseconds who controls the orderbook? forget about losing with Poly Bot Copy ⭣

Phosphen

96,184 просмотров • 5 месяцев назад

Ex-Citadel Quant Researcher on Trading Power & Gas — One of the Most Asymmetric Markets in the World Neel Somani (Neel Somani) — ex-Citadel commodities QR. Built the models the discretionary traders used to price power. "It's table stakes to put down seven figures of collateral in order to seriously trade power." We cover: - What a commodities QR actually does — building models traders use, sitting in PM meetings & how "slope" (your real cut of P&L) works - Where power edge comes from: congestion — the physics of a wire that heats up, droops, and can't carry more - How a hub trade gets built from the ground up: weather → demand → which units switch on → your price vs. the market's - Why blindly going long power is a structurally losing trade — skew assets always price above expected value - The anatomy of a blow-up: doubling down into the Feb 2021 Texas freeze as the price ran to $9,000/MWh - Why hedge funds trade power & gas but mostly steer clear of oil — geopolitics & risk you can't model - "Binding constraints" — the pricing model he carried off the grid and into startups, AI & supply chains - Why the guys who take risk for a living buy index funds with their own money Highlights: (00:00) Intro (01:12) Quant researcher execution models within multi-manager hedge funds (07:35) How transmission line congestion drives alpha in power markets (13:24) Capital intensity and managing risk profiles of high-skew assets (19:19) Why commodity desks prefer domestic power over geopolitical oil risk (22:55) Portfolio construction and risk mitigation during tail-risk freeze events (31:36) Capitalizing on the physical infrastructure constraints of AI data centers (36:25) How agentic architecture redefines software engineering and technical moats (43:04) Quant career opportunity cost relative to the AI paradigm shift (56:15) Variant views on venture multiples and agentic customer acquisition economics

Ex-Citadel Quant Researcher on Trading Power & Gas — One of the Most Asymmetric Markets in the World Neel Somani (Neel Somani) — ex-Citadel commodities QR. Built the models the discretionary traders used to price power. "It's table stakes to put down seven figures of collateral in order to seriously trade power." We cover: - What a commodities QR actually does — building models traders use, sitting in PM meetings & how "slope" (your real cut of P&L) works - Where power edge comes from: congestion — the physics of a wire that heats up, droops, and can't carry more - How a hub trade gets built from the ground up: weather → demand → which units switch on → your price vs. the market's - Why blindly going long power is a structurally losing trade — skew assets always price above expected value - The anatomy of a blow-up: doubling down into the Feb 2021 Texas freeze as the price ran to $9,000/MWh - Why hedge funds trade power & gas but mostly steer clear of oil — geopolitics & risk you can't model - "Binding constraints" — the pricing model he carried off the grid and into startups, AI & supply chains - Why the guys who take risk for a living buy index funds with their own money Highlights: (00:00) Intro (01:12) Quant researcher execution models within multi-manager hedge funds (07:35) How transmission line congestion drives alpha in power markets (13:24) Capital intensity and managing risk profiles of high-skew assets (19:19) Why commodity desks prefer domestic power over geopolitical oil risk (22:55) Portfolio construction and risk mitigation during tail-risk freeze events (31:36) Capitalizing on the physical infrastructure constraints of AI data centers (36:25) How agentic architecture redefines software engineering and technical moats (43:04) Quant career opportunity cost relative to the AI paradigm shift (56:15) Variant views on venture multiples and agentic customer acquisition economics

Ethan Kho

116,875 просмотров • 1 месяц назад

implied vol was 11.42% on Feb 19, 2020 realized vol over the next 30 days: 95.33% the market was pricing a normal week. what actually happened was one of the most violent moves in financial history that gap between implied and realized volatility is the single most important number quant desks watch implied vol is what options are pricing in for the future realized vol is what actually happened when implied is far below realized, options are cheap relative to what the market will actually do when implied is far above realized, options are expensive relative to what the market will actually do that spread has a name: the volatility risk premium Citadel, Susquehanna, Optiver built entire options desks around one question: what's the expected value of shorting overpriced vol vs buying underpriced vol? the scatter plot in the video shows every data point over years of history regression line: y = -0.73x + 18.5% translation: when implied vol is low, realized vol tends to explode. when implied vol is high, realized vol tends to collapse that's mean reversion in volatility itself. not price. vol retail buys calls hoping the stock goes up quant desks measure whether the option itself is mispriced relative to statistical expectation, and trade the premium one is a directional bet the other is arbitraging the gap between what people expect and what actually happens > volatility risk premium: documented in academic literature since the 1980s > average VRP on S&P 500: implied vol overprices realized vol by 3-5% historically > data: free from CBOE, Yahoo Finance, options chains > implementation: 20 lines of Python Feb 19, 2020 was a warning the math had been printing for weeks nobody in retail was reading it full breakdown in the video below

implied vol was 11.42% on Feb 19, 2020 realized vol over the next 30 days: 95.33% the market was pricing a normal week. what actually happened was one of the most violent moves in financial history that gap between implied and realized volatility is the single most important number quant desks watch implied vol is what options are pricing in for the future realized vol is what actually happened when implied is far below realized, options are cheap relative to what the market will actually do when implied is far above realized, options are expensive relative to what the market will actually do that spread has a name: the volatility risk premium Citadel, Susquehanna, Optiver built entire options desks around one question: what's the expected value of shorting overpriced vol vs buying underpriced vol? the scatter plot in the video shows every data point over years of history regression line: y = -0.73x + 18.5% translation: when implied vol is low, realized vol tends to explode. when implied vol is high, realized vol tends to collapse that's mean reversion in volatility itself. not price. vol retail buys calls hoping the stock goes up quant desks measure whether the option itself is mispriced relative to statistical expectation, and trade the premium one is a directional bet the other is arbitraging the gap between what people expect and what actually happens > volatility risk premium: documented in academic literature since the 1980s > average VRP on S&P 500: implied vol overprices realized vol by 3-5% historically > data: free from CBOE, Yahoo Finance, options chains > implementation: 20 lines of Python Feb 19, 2020 was a warning the math had been printing for weeks nobody in retail was reading it full breakdown in the video below

delost

28,202 просмотров • 1 месяц назад

Watch how I turned ₹4,000 into ₹2,00,000 in under 5 hours. 🚀👇 That's a 50x return. ✅ Before anyone says "gambling", ₹4,000 is less than what I often pay in trading fees on a single trade. The difference is position sizing. Don't go all-in on memecoins. You can allocate a tiny portion of my portfolio (around 0.1%) to asymmetric, high-risk, high-reward opportunities with a predefined risk. Most of these won't become 50x trades and that's okay. Because when one does, it can outperform the profits from 20+ normal trades combined. 🔥 It's not about blindly chasing pumps. It's about taking calculated bets where the downside is strictly limited, but the upside is practically unlimited. 👍

Watch how I turned ₹4,000 into ₹2,00,000 in under 5 hours. 🚀👇 That's a 50x return. ✅ Before anyone says "gambling", ₹4,000 is less than what I often pay in trading fees on a single trade. The difference is position sizing. Don't go all-in on memecoins. You can allocate a tiny portion of my portfolio (around 0.1%) to asymmetric, high-risk, high-reward opportunities with a predefined risk. Most of these won't become 50x trades and that's okay. Because when one does, it can outperform the profits from 20+ normal trades combined. 🔥 It's not about blindly chasing pumps. It's about taking calculated bets where the downside is strictly limited, but the upside is practically unlimited. 👍

Karan Singh Arora

35,230 просмотров • 1 месяц назад

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

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

56,779 просмотров • 3 месяцев назад

your backtest showed profit. so did mine then I ran 10,000 Monte Carlo simulations on that same strategy and found out we'd both read exactly one path out of 10,000 possible ones same 112 trades. reshuffled 10,000 times > worst outcome: -31% drawdown, 14 months underwater > 5th percentile: -18% max drawdown > median: +19% annual return > 95th percentile: +28% return strategy has a real edge but 1 in 20 traders running it hits a path that looks like failure for over a year most quit before month 14. blame the strategy, market, signals wasn't any of that here's a number that actually ends accounts: probability of ruin at 2% risk per trade: 0.3% bump to 3%: 4.1% 13x more likely to blow up from changing one input - not worse entries, not worse signals, just 1% more size most "failed" systems weren't failing. they were good systems at wrong size on a bad sequence the trader never knew existed one Monte Carlo run would've told them: this drawdown is normal, keep trading Bookmark this info most traders will never run it. they'll read the backtest once, start live, hit month 4, and wonder why their "proven" system broke it didn't break. they just never knew what they were holding

your backtest showed profit. so did mine then I ran 10,000 Monte Carlo simulations on that same strategy and found out we'd both read exactly one path out of 10,000 possible ones same 112 trades. reshuffled 10,000 times > worst outcome: -31% drawdown, 14 months underwater > 5th percentile: -18% max drawdown > median: +19% annual return > 95th percentile: +28% return strategy has a real edge but 1 in 20 traders running it hits a path that looks like failure for over a year most quit before month 14. blame the strategy, market, signals wasn't any of that here's a number that actually ends accounts: probability of ruin at 2% risk per trade: 0.3% bump to 3%: 4.1% 13x more likely to blow up from changing one input - not worse entries, not worse signals, just 1% more size most "failed" systems weren't failing. they were good systems at wrong size on a bad sequence the trader never knew existed one Monte Carlo run would've told them: this drawdown is normal, keep trading Bookmark this info most traders will never run it. they'll read the backtest once, start live, hit month 4, and wonder why their "proven" system broke it didn't break. they just never knew what they were holding

Livsun

29,454 просмотров • 2 месяцев назад

wall street quant got fired and built a bot that trades against you on polymarket $724k in one week I understood how he did it 4,200 lines of Rust, zero machine learning pulls Deribit options implied volatility via WebSocket at 8ms converts IV surface to probability distribution using Black-Scholes polls polymarket CLOB prices every 200ms when mispricing exceeds 5¢ on strikes beyond 2.1 sigma → execution layer scatters parallel market orders across correlated markets in single async batch PostgreSQL for position tracking, Redis for state cache runs on a single AWS instance tokio async runtime, two data streams, compiled binary the bot finds tail-risk markets where polymarket odds diverge from options-derived probability and executes before anyone recalculates I started copying his trades via PolyGun and have already earned $1k in a couple of days

wall street quant got fired and built a bot that trades against you on polymarket $724k in one week I understood how he did it 4,200 lines of Rust, zero machine learning pulls Deribit options implied volatility via WebSocket at 8ms converts IV surface to probability distribution using Black-Scholes polls polymarket CLOB prices every 200ms when mispricing exceeds 5¢ on strikes beyond 2.1 sigma → execution layer scatters parallel market orders across correlated markets in single async batch PostgreSQL for position tracking, Redis for state cache runs on a single AWS instance tokio async runtime, two data streams, compiled binary the bot finds tail-risk markets where polymarket odds diverge from options-derived probability and executes before anyone recalculates I started copying his trades via PolyGun and have already earned $1k in a couple of days

Argona

110,016 просмотров • 6 месяцев назад

i built a 2 agent system using OpenClaw and Monte Carlo simulation > one agent predicts gold price > second agent bets on polymarket > second agent takes profit $1,400 → $17,900 in 72 hours saw a market on polymarket: "Will gold hit $3,000 by March 15?" price was sitting at 18¢ seemed random until i remembered Monte Carlo exists gave OpenClaw a task: "run 10,000 Monte Carlo simulations on gold price movement, calculate probability of hitting $3,000, pass results to trading agent" the architecture: > Agent 1 (Simulation Engine): - pulls historical gold volatility data - runs 10,000 price path simulations - factors in: Fed policy, geopolitical tension, USD strength - outputs: 73.4% probability gold hits $3,000 > Agent 2 (Trade Executor): > receives probability from Agent 1 > compares to polymarket odds (18¢ = 18% implied probability) > detects massive mispricing (73% vs 18%) > xecutes position hour 6: entered YES at 18¢ with $1,400 hour 24: gold jumps on Iran tensions, polymarket updates to 41¢ hour 48: Fed hints at rate cuts, simulation re-runs, now shows 81% probability hour 56: polymarket hits 67¢, Agent 2 adds to position hour 72: gold touches $2,987, market resolves YES at 94¢ final: $1,400 → $17,900 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬: polymarket prices are just crowd sentiment Monte Carlo is actual math > when math says 73% and crowd says 18% > that's not a trade > that's free money the simulation factored in: - 500+ historical gold price scenarios - current macro conditions - geopolitical risk premium - correlation with treasury yields ran this 4 more times on different markets: "Bitcoin above $70K by month end" - simulation: 62%, market: 31% → won "Unemployment rate above 4.2%" - simulation: 44%, market: 68% → bet NO, won "Tesla stock hits $250" - simulation: 28%, market: 52% → bet NO, won "Trump announces tariffs this week" - simulation can't model politics → skipped 7 trades total 6 wins 1 skip (non-quantifiable event) the edge is simple: most traders bet on vibes i'm betting on 10,000 simulated futures best polymarket traders use only tradefox: does anyone else realize polymarket is just mispriced probability distributions?

i built a 2 agent system using OpenClaw and Monte Carlo simulation > one agent predicts gold price > second agent bets on polymarket > second agent takes profit $1,400 → $17,900 in 72 hours saw a market on polymarket: "Will gold hit $3,000 by March 15?" price was sitting at 18¢ seemed random until i remembered Monte Carlo exists gave OpenClaw a task: "run 10,000 Monte Carlo simulations on gold price movement, calculate probability of hitting $3,000, pass results to trading agent" the architecture: > Agent 1 (Simulation Engine): - pulls historical gold volatility data - runs 10,000 price path simulations - factors in: Fed policy, geopolitical tension, USD strength - outputs: 73.4% probability gold hits $3,000 > Agent 2 (Trade Executor): > receives probability from Agent 1 > compares to polymarket odds (18¢ = 18% implied probability) > detects massive mispricing (73% vs 18%) > xecutes position hour 6: entered YES at 18¢ with $1,400 hour 24: gold jumps on Iran tensions, polymarket updates to 41¢ hour 48: Fed hints at rate cuts, simulation re-runs, now shows 81% probability hour 56: polymarket hits 67¢, Agent 2 adds to position hour 72: gold touches $2,987, market resolves YES at 94¢ final: $1,400 → $17,900 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐦𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐦𝐢𝐬𝐬: polymarket prices are just crowd sentiment Monte Carlo is actual math > when math says 73% and crowd says 18% > that's not a trade > that's free money the simulation factored in: - 500+ historical gold price scenarios - current macro conditions - geopolitical risk premium - correlation with treasury yields ran this 4 more times on different markets: "Bitcoin above $70K by month end" - simulation: 62%, market: 31% → won "Unemployment rate above 4.2%" - simulation: 44%, market: 68% → bet NO, won "Tesla stock hits $250" - simulation: 28%, market: 52% → bet NO, won "Trump announces tariffs this week" - simulation can't model politics → skipped 7 trades total 6 wins 1 skip (non-quantifiable event) the edge is simple: most traders bet on vibes i'm betting on 10,000 simulated futures best polymarket traders use only tradefox: does anyone else realize polymarket is just mispriced probability distributions?

ZER

149,335 просмотров • 5 месяцев назад

The $20 billion ING bond veteran just exposed the ultimate trap in modern asset allocation. One word: Fragility. It's the entire reason your diversification fails when you need it most, and the reason the 60/40 model is structurally broken. 73 minutes from Alfonso Peccatiello. Three things click after this one: 1/ The negative correlation property between bonds and stocks only exists when core inflation is predictably below 3%. Above that, bonds completely fail as a portfolio diversifier. 2/ Volatility in highly leveraged short-term trades forces a cascade of stop-losses among marginal buyers, putting the very base of the fixed income pyramid under constant strain. 3/ True macro risk parity requires an explicitly leveraged defensive framework -- using futures to free up raw capital so you can scale entirely non-correlated streams of return. Interview 1 of 1 and fully free.. After this, "bonds are safe" stops being an excuse. You'll see where the pyramid breaks before it does. Bookmark & check it today.

The $20 billion ING bond veteran just exposed the ultimate trap in modern asset allocation. One word: Fragility. It's the entire reason your diversification fails when you need it most, and the reason the 60/40 model is structurally broken. 73 minutes from Alfonso Peccatiello. Three things click after this one: 1/ The negative correlation property between bonds and stocks only exists when core inflation is predictably below 3%. Above that, bonds completely fail as a portfolio diversifier. 2/ Volatility in highly leveraged short-term trades forces a cascade of stop-losses among marginal buyers, putting the very base of the fixed income pyramid under constant strain. 3/ True macro risk parity requires an explicitly leveraged defensive framework -- using futures to free up raw capital so you can scale entirely non-correlated streams of return. Interview 1 of 1 and fully free.. After this, "bonds are safe" stops being an excuse. You'll see where the pyramid breaks before it does. Bookmark & check it today.

slash1s

11,372 просмотров • 2 месяцев назад

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 drift velocity: μ/σ, the ratio of expected return to volatility when β is high the market is moving with conviction when β decays toward zero the trend is losing energy before the chart shows any sign of it α measures how fast that velocity is changing τ is the time structure of the regime ∇τ is the gradient, the rate at which the regime itself is shifting the 3D surface on screen is not decoration the red peaks are where momentum is concentrated and unstable the blue basin is where the system is calm and mean-reverting the yellow marker is where the S&P sat at the moment of the snapshot the model's output: position sizing optimization not buy or sell. how much exposure to hold given the current position on that surface the bottom chart shows it: blue line is the model, white line is buy & hold same asset, same period, different sizing at every point based on where the dynamics equation said the market was > time dynamics models: rooted in physics, applied to finance since the 1990s > drift-to-volatility ratio: standard risk metric at Two Sigma, AQR, Man Group > this exact framework: free, public, 60 seconds in this video > what retail uses instead: lagging indicators that measure the past, not the velocity of the present retail asks "is the market going up or down?" this model asks "how fast is the market's own energy decaying and where on the surface are we right now?" completely different question. completely different result full breakdown in the video below

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 drift velocity: μ/σ, the ratio of expected return to volatility when β is high the market is moving with conviction when β decays toward zero the trend is losing energy before the chart shows any sign of it α measures how fast that velocity is changing τ is the time structure of the regime ∇τ is the gradient, the rate at which the regime itself is shifting the 3D surface on screen is not decoration the red peaks are where momentum is concentrated and unstable the blue basin is where the system is calm and mean-reverting the yellow marker is where the S&P sat at the moment of the snapshot the model's output: position sizing optimization not buy or sell. how much exposure to hold given the current position on that surface the bottom chart shows it: blue line is the model, white line is buy & hold same asset, same period, different sizing at every point based on where the dynamics equation said the market was > time dynamics models: rooted in physics, applied to finance since the 1990s > drift-to-volatility ratio: standard risk metric at Two Sigma, AQR, Man Group > this exact framework: free, public, 60 seconds in this video > what retail uses instead: lagging indicators that measure the past, not the velocity of the present retail asks "is the market going up or down?" this model asks "how fast is the market's own energy decaying and where on the surface are we right now?" completely different question. completely different result full breakdown in the video below

delost

72,275 просмотров • 1 месяц назад