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sopersone

@sopersone • 3,666 subscribers

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

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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

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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.

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.

159,991 просмотров • 1 месяц назад

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 просмотров • 1 месяц назад

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

138,656 просмотров • 2 месяцев назад

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

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:

56,426 просмотров • 1 месяц назад

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

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

79,095 просмотров • 2 месяцев назад

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

54,967 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

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

24,364 просмотров • 2 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:

15,585 просмотров • 2 месяцев назад

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