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sopersone

@sopersone • 4,092 subscribers

Inexperienced designer | living stress-free | learning quant trading | prediction markets researcher https://t.co/MfpZB4Z31V

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I spent 48h to find this ''bot trading wallet'' on Polymarket He made 10,057 trades in one month and earned $87,000 > this wallet is an arbitrage bot > trades only BTC Up or Down (5 min) only > always use the same strategy And here's how it trades: > waits until one of the outcomes (YES/NO) falls below 35¢ > buy more when the average price is low > watch how the opposite outcome moves > makes sure the total average prices are under 99¢ > if he doesn't have a hedge, just closing the position with no losses This is exactly how algo-trading works His profile:

37,390 views

THAT PRETTY 3D QUANT CHART IS JUST A WEIGHTED SUM OF SIGNALS. THEY'VE GATEKEPT IT FOR 40 YEARS That gorgeous formula on screen is doing one simple thing. It scores every stock in a universe on a blend of signals, then goes long the strongest and short the weakest. That is the entire quant game, hiding inside one Sigma. The signals even have names. Free cash flow yield and earnings yield for value, QMJ profitability and Piotroski F-score for quality, plus momentum, mean reversion and volatility. Minara ships 500+ of these factors and lets you blend them into that exact composite by typing one sentence. The PhD math is real. The wall in front of it is gone. I ran this exact cross-sectional composite on 30 stocks & posted every metric in the article. Check it ↓

THAT PRETTY 3D QUANT CHART IS JUST A WEIGHTED SUM OF SIGNALS. THEY'VE GATEKEPT IT FOR 40 YEARS That gorgeous formula on screen is doing one simple thing. It scores every stock in a universe on a blend of signals, then goes long the strongest and short the weakest. That is the entire quant game, hiding inside one Sigma. The signals even have names. Free cash flow yield and earnings yield for value, QMJ profitability and Piotroski F-score for quality, plus momentum, mean reversion and volatility. Minara ships 500+ of these factors and lets you blend them into that exact composite by typing one sentence. The PhD math is real. The wall in front of it is gone. I ran this exact cross-sectional composite on 30 stocks & posted every metric in the article. Check it ↓

33,919 views

I launched a weather trading bot on Polymarket with $100 -> $8,000 here is how it works and how to copy it most people on polymarket price weather markets randomly based on vibes or a phone app but there is NOAA - a government agency that publishes free forecasts with 94%+ accuracy the result? you regularly see something like this: > NOAA gives a 94% probability that NYC will reach 74°F on Saturday > polymarket prices it at 11¢ the bot buys at 11¢ sells when the market corrects to 45¢ that is 4x on an almost guaranteed outcome how to launch it in 5 steps: > install OpenClaw on your pc > connect it to ChatGPT Plus + a telegram bot > create an agent on simmer markets deposit $100 > install the weather trading skill with: clawhub install simmer-weather > send the config to the bot and it starts trading the bot scans 6 cities every 2 minutes non-stop example of a real trader: profile: +$75,055 just from weather markets zero emotions. fully automated the window is still open but not forever

64,986 views

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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.

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.

72,810 views • 1 month ago

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.

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.

160,999 views • 4 months ago

Someone read my guide on building a Polymarket trading bot in Python built it and just sent me this video 👇 I’m happy haha

Someone read my guide on building a Polymarket trading bot in Python built it and just sent me this video 👇 I’m happy haha

139,627 views • 5 months ago

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the difference between $332 and $2219 trade:

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the difference between $332 and $2219 trade:

121,533 views • 4 months ago

> Claude coders right now > Python coders right now > Polymarket traders right now

> Claude coders right now > Python coders right now > Polymarket traders right now

79,374 views • 5 months ago

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

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:

65,108 views • 4 months ago

$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:

57,212 views • 4 months ago

Another guy building a Polymarket trading bot in Python I am very very happy hahaha

Another guy building a Polymarket trading bot in Python I am very very happy hahaha

55,027 views • 5 months ago

market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI

market makers aren't quoting price they're running an inventory optimization formula, and every bid/ask you've ever traded against was its output formula is free, been public since 2008, and retail trading has never once mentioned it NYU professor Marco Avellaneda published the model - 14 pages, zero paywall, on SSRN reservation price: r = mid - inventory × γ × σ² × (T-t) when a desk holds too much of one side, they shift their entire spread to push flow the other direction. you were always a variable in that calculation 4 resources to run this yourself: > avellaneda & stoikov 2008 - "high-frequency trading in a limit order book" - free on SSRN > github: search "avellaneda stoikov python" - clean implementations under 200 lines > tardis - historical level 2 order book data, exact inputs the model runs on > lobsterdata - NASDAQ order book reconstructions, free academic access Bookmark this before placing another trade optimal spread: δ = γσ²(T-t) + (2/γ)ln(1 + γ/κ) γ = risk aversion, σ = volatility, T-t = time horizon, κ = arrival rate that second term - (2/γ)ln(1 + γ/κ) - is the profit extraction piece retail never sees you've never been on the right side of a spread - you've been the inventory problem they were solving What used to require manual calculations now takes just a few seconds with AI

11,887 views • 1 month ago

I think this is one of the best automated trading bots on Polymarket since March 2026 it has made over 101,702 predictions trades exclusively on Bitcoin Up/Down markets. Total profit: $190,392 best trade: $216 → $1,709 this is one of the most consistent automated traders I've seen on Polymarket pofile:

I think this is one of the best automated trading bots on Polymarket since March 2026 it has made over 101,702 predictions trades exclusively on Bitcoin Up/Down markets. Total profit: $190,392 best trade: $216 → $1,709 this is one of the most consistent automated traders I've seen on Polymarket pofile:

10,707 views • 1 month ago

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the line between catching a soft breach early and discovering the edge died eight months ago trade:

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the line between catching a soft breach early and discovering the edge died eight months ago trade:

16,874 views • 2 months ago

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

22,770 views • 5 months ago

Chinese quant genius unlocked 100% of his brain's potential at just 22 years old, living in a dorm room and spending only 10 days, he built a money-making machine raised $4.1m in investment and earned the respect of 18,000⭐developers on GitHub then he tested his simulator on prediction markets the result: $85,696 in pure profit in one week check his trade history yourself: his wallet: 0x17559efac103ac7f361be37ec0b93888d4c55aac

Chinese quant genius unlocked 100% of his brain's potential at just 22 years old, living in a dorm room and spending only 10 days, he built a money-making machine raised $4.1m in investment and earned the respect of 18,000⭐developers on GitHub then he tested his simulator on prediction markets the result: $85,696 in pure profit in one week check his trade history yourself: his wallet: 0x17559efac103ac7f361be37ec0b93888d4c55aac

24,430 views • 5 months ago

THIS GUY CONNECTED A NASA SATELLITE TO CLAUDE AND MADE $7,778 Pointed satellite data at 6 countries, the bot found anomalies on its own and traded geopolitics on Polymarket Dropped the entire bot on GitHub, grab it and run 👇

THIS GUY CONNECTED A NASA SATELLITE TO CLAUDE AND MADE $7,778 Pointed satellite data at 6 countries, the bot found anomalies on its own and traded geopolitics on Polymarket Dropped the entire bot on GitHub, grab it and run 👇

20,615 views • 5 months ago

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:

15,781 views • 4 months ago

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

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:

15,585 views • 5 months ago

It took me a month of working with Polymarket data to build this bot most bots trade everything mine rejects 80% of markets it doesn't predict btc direction it buys BOTH sides when each gets temporarily cheap > YES at 48c > NO at $45c > pair cost: $93c > one side always pays $1.00 > guaranteed 7c profit per pair but entering every market is how you blow up so the bot runs three filters before every trade > base rate: compares current odds to 2 years of historical data > Kelly criterion: calculates EV, if negative - skips > liquidity and spread: volume below $50k or spread above 3% - skips 60 days live: 847 markets scanned 156 entries 71.4% win rate +$2,847 net profit maKe your predictions -> most people lose money not because they trade badly but because they trade where they shouldn't the bot just learned to say "no"

It took me a month of working with Polymarket data to build this bot most bots trade everything mine rejects 80% of markets it doesn't predict btc direction it buys BOTH sides when each gets temporarily cheap > YES at 48c > NO at $45c > pair cost: $93c > one side always pays $1.00 > guaranteed 7c profit per pair but entering every market is how you blow up so the bot runs three filters before every trade > base rate: compares current odds to 2 years of historical data > Kelly criterion: calculates EV, if negative - skips > liquidity and spread: volume below $50k or spread above 3% - skips 60 days live: 847 markets scanned 156 entries 71.4% win rate +$2,847 net profit maKe your predictions -> most people lose money not because they trade badly but because they trade where they shouldn't the bot just learned to say "no"

11,197 views • 4 months ago

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