Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

This 1-hour MIT Probability Theory lecture is the mathematical foundation behind the 3 formulas top traders use > Law of Large Numbers - a tiny edge compounds into profit over enough trades > log-normal distribution - why modelling markets with normal distribution fails every time > Shannon entropy -... show more

Mr. Buzzoni

20,760 subscribers

38,237 Aufrufe • vor 3 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Jane Street pays $750K / year to traders who can apply probability theory in quantitative trading. This 1-hour MIT lecture on "Probability Theory" explains how to master this concept for trading - for free. Bookmark it & give it 1-hour on it today. It will change the way you analyze markets. Then read the article below ↓

Jane Street pays $750K / year to traders who can apply probability theory in quantitative trading. This 1-hour MIT lecture on "Probability Theory" explains how to master this concept for trading - for free. Bookmark it & give it 1-hour on it today. It will change the way you analyze markets. Then read the article below ↓

Movez

20,205 Aufrufe • vor 1 Monat

1 hour of this MIT lecture = outcompeting most people applying for quant and trading roles Black-Scholes and Risk Neutral Valuation sounds intimidating > it's not. it's the foundation of how every prediction - market is priced > most traders have never heard of it the ones consistently profitable usually have > watch this once then re-read every market price you see differently 👇

1 hour of this MIT lecture = outcompeting most people applying for quant and trading roles Black-Scholes and Risk Neutral Valuation sounds intimidating > it's not. it's the foundation of how every prediction - market is priced > most traders have never heard of it the ones consistently profitable usually have > watch this once then re-read every market price you see differently 👇

Mr. Buzzoni

23,338 Aufrufe • vor 3 Monaten

A Chinese MIT professor explains how financial and prediction markets actually work. Everything you need to understand both. Most people lose money in markets not because they pick wrong - but because they dont understand the structure they're playing against. Who sets prices. How liquidity works. Why derivatives exist. How market makers profit from every trade regardless of direction. This lecture explains the actual mechanics. After it you'll see every Polymarket market differently - not as a bet, but as a pricing problem with a correct answer that the crowd hasn't found yet. That gap between the crowd's price and the correct price is where money gets made. One hour. Jake Xia. MIT. Bookmark this.

A Chinese MIT professor explains how financial and prediction markets actually work. Everything you need to understand both. Most people lose money in markets not because they pick wrong - but because they dont understand the structure they're playing against. Who sets prices. How liquidity works. Why derivatives exist. How market makers profit from every trade regardless of direction. This lecture explains the actual mechanics. After it you'll see every Polymarket market differently - not as a bet, but as a pricing problem with a correct answer that the crowd hasn't found yet. That gap between the crowd's price and the correct price is where money gets made. One hour. Jake Xia. MIT. Bookmark this.

Atenov int.

25,654 Aufrufe • vor 3 Monaten

> If you’re reading my article about probability, prediction markets and tools like MiroFish > this 1 hour Stanford lecture “Bayesian Networks and Learning” is worth watching > one of the best short introductions to how beliefs update with new information > highly recommend

> If you’re reading my article about probability, prediction markets and tools like MiroFish > this 1 hour Stanford lecture “Bayesian Networks and Learning” is worth watching > one of the best short introductions to how beliefs update with new information > highly recommend

Mr. Buzzoni

59,250 Aufrufe • vor 4 Monaten

This trader built a trading bot with Claude and made $295,000 on Polymarket His bot makes 28.8 trades per hour or 1.58 trades per minute and trades the structure of related markets around the same underlying asset price His strategy is simple: > Compares the current ETH/SOL/XRP price with the price on Polymarket > Buys when it sees a gap between fair probability and the order book price > Uses arbitrage if there is edge This guy’s Polymarket account: Using this strategy, he generates consistent profit and grows his capital

This trader built a trading bot with Claude and made $295,000 on Polymarket His bot makes 28.8 trades per hour or 1.58 trades per minute and trades the structure of related markets around the same underlying asset price His strategy is simple: > Compares the current ETH/SOL/XRP price with the price on Polymarket > Buys when it sees a gap between fair probability and the order book price > Uses arbitrage if there is edge This guy’s Polymarket account: Using this strategy, he generates consistent profit and grows his capital

VALIX

77,250 Aufrufe • vor 2 Monaten

🚨 Epic Breakouts Explained: Gold, Silver & Bitcoin In this episode of Talking Trades, Northstar & Patrick Karim reveal the patterns behind some of the biggest moves in markets, why technical analysis beats guessing, and how multi-year consolidations set the stage for explosive gains. Learn how to identify high-probability entry points, read critical support & resistance levels, and understand why timing your trades beats chasing price. Watch now:

🚨 Epic Breakouts Explained: Gold, Silver & Bitcoin In this episode of Talking Trades, Northstar & Patrick Karim reveal the patterns behind some of the biggest moves in markets, why technical analysis beats guessing, and how multi-year consolidations set the stage for explosive gains. Learn how to identify high-probability entry points, read critical support & resistance levels, and understand why timing your trades beats chasing price. Watch now:

Kinesis Money

1,158,317 Aufrufe • vor 6 Monaten

Polymarket just got mathematically robbed. Top 0.04% already took 70% of all the money ($3.7B) thanks to 4 formulas from this article below. Open the leaderboard and there he is. [0x8dxd]: $2,285,751 ALL-TIME profit. Joined Dec 2025. $41.2K biggest win. 31,570 predictions. Not luck. Not "feeling the market". This is a bot running strictly on Lunar’s formulas on autopilot: Formula 1 - Expected Value (When to Enter) Contract at 40¢, but your real probability assessment is 60% -> +20¢ edge per dollar. Claude calculates this in seconds. Formula 2 - Kelly Criterion (How Much to Bet) f = (p·b − q)/b* (Quarter Kelly - that's why he doesn't blow up and compounds without any emotion) Formula 3 - Bayesian Updating (How to Change Your Mind) P(H|E) = [P(E|H) × P(H)] / P(E) Instantly rebuilds probability on any news. Formula 4 - Log Returns (Real Profit Calculation) Regular arithmetic lies. Only log returns show the real picture. Scans 50+ markets at once, enters only 15-min BTC Up/Down with real edge (where [0x8dxd] prints +157%, +207%, +181%), sleeps the rest of the time. 87% of wallets are in the red. A few such bots take everything. And this guy didn't just write an article - he handed out the blueprint that's already working for [0x8dxd]. Don't want to code it yourself? Just copy this trader with You can runs exactly this system: the same 4 formulas + Claude + auto-copying top math. Add his wallet: 0x63ce342161250d705dc0b16df89036c8e5f9ba9a to [ and start track/copy him right now. 4 formulas from Wikipedia + Claude brain + Kreo execution = meta 2026. The math doesn't sleep. Your FOMO does. Save and use this.

Polymarket just got mathematically robbed. Top 0.04% already took 70% of all the money ($3.7B) thanks to 4 formulas from this article below. Open the leaderboard and there he is. [0x8dxd]: $2,285,751 ALL-TIME profit. Joined Dec 2025. $41.2K biggest win. 31,570 predictions. Not luck. Not "feeling the market". This is a bot running strictly on Lunar’s formulas on autopilot: Formula 1 - Expected Value (When to Enter) Contract at 40¢, but your real probability assessment is 60% -> +20¢ edge per dollar. Claude calculates this in seconds. Formula 2 - Kelly Criterion (How Much to Bet) f = (p·b − q)/b* (Quarter Kelly - that's why he doesn't blow up and compounds without any emotion) Formula 3 - Bayesian Updating (How to Change Your Mind) P(H|E) = [P(E|H) × P(H)] / P(E) Instantly rebuilds probability on any news. Formula 4 - Log Returns (Real Profit Calculation) Regular arithmetic lies. Only log returns show the real picture. Scans 50+ markets at once, enters only 15-min BTC Up/Down with real edge (where [0x8dxd] prints +157%, +207%, +181%), sleeps the rest of the time. 87% of wallets are in the red. A few such bots take everything. And this guy didn't just write an article - he handed out the blueprint that's already working for [0x8dxd]. Don't want to code it yourself? Just copy this trader with You can runs exactly this system: the same 4 formulas + Claude + auto-copying top math. Add his wallet: 0x63ce342161250d705dc0b16df89036c8e5f9ba9a to [ and start track/copy him right now. 4 formulas from Wikipedia + Claude brain + Kreo execution = meta 2026. The math doesn't sleep. Your FOMO does. Save and use this.

slash1s

20,164 Aufrufe • vor 4 Monaten

This guy built a trading bot with Claude and made $228,000 on Polymarket His bot makes 141 trades per hour or 2.35 trades per minute and trades the microstructure of short Up/Down markets with a weighted edge of +2.02% His strategy has 3 layers: 1. Trades mispricing using his probability model with a median entry price of 0.33c 2. Buys nearly resolved outcomes at 0.9-0.99c if their fair price should already be around $1.00 3. Uses arbitrage if it sees edge This guy’s Polymarket account: Using this strategy, he turned $36.3k into $228k in 3 months

This guy built a trading bot with Claude and made $228,000 on Polymarket His bot makes 141 trades per hour or 2.35 trades per minute and trades the microstructure of short Up/Down markets with a weighted edge of +2.02% His strategy has 3 layers: 1. Trades mispricing using his probability model with a median entry price of 0.33c 2. Buys nearly resolved outcomes at 0.9-0.99c if their fair price should already be around $1.00 3. Uses arbitrage if it sees edge This guy’s Polymarket account: Using this strategy, he turned $36.3k into $228k in 3 months

VALIX

23,980 Aufrufe • vor 2 Monaten

1¢ contract on Polymarket returns 43¢ per dollar a slot machine returns 90 cents • 72 million trades exposed this and nobody is talking about it: > takers (market orders) lose −1.12% per trade > makers (limit orders) earn +1.12% per trade > NO outperforms YES at 69 out of 99 price levels > people buy YES on their team, their candidate... > the quants collect the other side One equation connects all of this - Markov chains The same math behind Google's PageRank, nuclear bomb simulations, and every LLM This 1 hour MIT lecture on "Markov Chains in Finance" will teach you more about prediction markets than every "alpha" article you've ever bookmarked Bookmark & watch it once. Then read the full article below, you won't trade the same way again ↓

1¢ contract on Polymarket returns 43¢ per dollar a slot machine returns 90 cents • 72 million trades exposed this and nobody is talking about it: > takers (market orders) lose −1.12% per trade > makers (limit orders) earn +1.12% per trade > NO outperforms YES at 69 out of 99 price levels > people buy YES on their team, their candidate... > the quants collect the other side One equation connects all of this - Markov chains The same math behind Google's PageRank, nuclear bomb simulations, and every LLM This 1 hour MIT lecture on "Markov Chains in Finance" will teach you more about prediction markets than every "alpha" article you've ever bookmarked Bookmark & watch it once. Then read the full article below, you won't trade the same way again ↓

Сarm1ne

422,200 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 3 Monaten

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,315 Aufrufe • vor 2 Monaten

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

433,431 Aufrufe • vor 1 Monat

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

In 1948, Claude Shannon invented the math behind every LLM you use today. He tested it by making his wife guess the next letter in a book. A Stanford-trained mathematician just released a 32-minute walkthrough of this exact history - and why the "next-token prediction" story of GPT-5 is actually wrong. Bookmark & watch this weekend. The alternative is a graduate info-theory course + 3 semesters of your life.

fintex

285,466 Aufrufe • vor 20 Tagen

My Polymarket bot was profitable But I kept losing to one thing I couldn't see. Someone always knew before me. Political markets. Price sitting at 0.52 for 3 days. Then in 15 minutes - snaps to 0.71. Huge wallet. No news. I'm already on the wrong side. It happened 6 times in one month. Cost me $4,200. Then I found a 77-year-old formula that explained exactly what was happening. Claude Shannon. MIT professor. Invented the math behind every ZIP file, every modem, every JPEG on Earth. In 1986, Barron's ranked 1,026 mutual funds. Shannon's personal portfolio beat 1,025 of them. He wasn't a trader. He was measuring something nobody else measured. Edge in bits. Not dollars. Bits. His student took it to Vegas first. Won $11,000 in a weekend. Then to Wall Street. 19% annually. 20 years. Zero losing seasons. I built three tools from their framework. Tool 1 - KL-divergence. Scans every open Polymarket market. Returns one number: how many bits of edge you have vs the market price. Below 0.05 - skip. Above 0.10 - trade. Simple filter. Brutal results. D_KL = p · log₂(p/q) + (1-p) · log₂((1-p)/(1-q)) Tool 2 - Max-entropy fusion. 4 signals pointing in different directions. This collapses them into one honest probability - without overfitting. Mathematically impossible to overfit. Jaynes proved it in 1957. Tool 3 - Entropy collapse detector. This one changed everything. Insiders don't announce themselves. But entropy does. Normal market: entropy drifts slowly. Insider enters: entropy collapses. Fast. Sharp. Before any news. Alert if |dH/dt| > 3 · σH Oct 13, 2024. "Will Trump win Pennsylvania?" Sitting at 0.52. One wallet opens a massive position. Entropy drops 0.093 bits in 15 minutes - a 7-sigma event. No public news. 47 minutes later - the news breaks. That wallet nets $340,000. The SEC uses this exact method on equities. Almost nobody runs it on Polymarket. I ran it. Week 1: calibrating signals. +$800. Week 2: first insider alerts firing. +$3,400. Week 3: stopped fighting the tape, followed entropy. +$5,900. Week 4: fully automated. +$7,200. +$17,300. Four weeks. A formula from 1948. Copytrade here: Everyone watches price. Quants watch entropy. The insider arrives an hour before the news. Now I see them coming.

My Polymarket bot was profitable But I kept losing to one thing I couldn't see. Someone always knew before me. Political markets. Price sitting at 0.52 for 3 days. Then in 15 minutes - snaps to 0.71. Huge wallet. No news. I'm already on the wrong side. It happened 6 times in one month. Cost me $4,200. Then I found a 77-year-old formula that explained exactly what was happening. Claude Shannon. MIT professor. Invented the math behind every ZIP file, every modem, every JPEG on Earth. In 1986, Barron's ranked 1,026 mutual funds. Shannon's personal portfolio beat 1,025 of them. He wasn't a trader. He was measuring something nobody else measured. Edge in bits. Not dollars. Bits. His student took it to Vegas first. Won $11,000 in a weekend. Then to Wall Street. 19% annually. 20 years. Zero losing seasons. I built three tools from their framework. Tool 1 - KL-divergence. Scans every open Polymarket market. Returns one number: how many bits of edge you have vs the market price. Below 0.05 - skip. Above 0.10 - trade. Simple filter. Brutal results. D_KL = p · log₂(p/q) + (1-p) · log₂((1-p)/(1-q)) Tool 2 - Max-entropy fusion. 4 signals pointing in different directions. This collapses them into one honest probability - without overfitting. Mathematically impossible to overfit. Jaynes proved it in 1957. Tool 3 - Entropy collapse detector. This one changed everything. Insiders don't announce themselves. But entropy does. Normal market: entropy drifts slowly. Insider enters: entropy collapses. Fast. Sharp. Before any news. Alert if |dH/dt| > 3 · σH Oct 13, 2024. "Will Trump win Pennsylvania?" Sitting at 0.52. One wallet opens a massive position. Entropy drops 0.093 bits in 15 minutes - a 7-sigma event. No public news. 47 minutes later - the news breaks. That wallet nets $340,000. The SEC uses this exact method on equities. Almost nobody runs it on Polymarket. I ran it. Week 1: calibrating signals. +$800. Week 2: first insider alerts firing. +$3,400. Week 3: stopped fighting the tape, followed entropy. +$5,900. Week 4: fully automated. +$7,200. +$17,300. Four weeks. A formula from 1948. Copytrade here: Everyone watches price. Quants watch entropy. The insider arrives an hour before the news. Now I see them coming.

Trackmind

13,229 Aufrufe • vor 3 Monaten

Debunking Coffeezilla’s prediction market claims In his recent video “prediction markets aren’t just gambling,” Coffeezilla made the three untrue or misleading claims below: 1) “The only way you get the news early, by the way, is if it’s insider trading.” 2) “All these prediction markets are doing is aggregating sentiment on the news.” 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” I want to start by saying that I’ve really enjoyed Coffeezilla’s content in the past, and I appreciate him blacking out my name in the tweet he screenshared. I also watched the full video and agreed with parts of it (and disagreed with other parts). That said, these three claims are egregiously incorrect, and I want to correct the record. I hope Coffeezilla reads this and reconsiders these points 1) “The only way you get the news early, by the way, is if it’s insider trading.” Merriam-Webster defines insider trading as “the illegal use of information available only to insiders in order to make a profit in financial trading.” This claim is wrong because it is clearly possible to get information early in entirely legal ways. For example, in CPI inflation markets, someone might notice prices rising on goods they regularly buy, or a sophisticated trader might aggregate pricing data across many products and form a forecast before the CPI release. Journalists may later report on inflation, but the information existed beforehand. Markets also react faster to sudden events, like a Trump Truth Social post or an earthquake, than journalists do. Traders are financially incentivized to react in seconds; journalists are not. Recent high-profile examples include Nobel Peace Prize, Spotify, and Time POTY markets, where traders had information before the news broke. In the Nobel case, there was disinformation claiming insider trading, but as far as most observers can tell, the information was obtained legally via web-scraping. Even if the Nobel Committee disliked it, legally obtained information is fair game. 2) “All these prediction markets are doing is aggregating sentiment on the news.” This is easy to debunk. Prediction markets do aggregate information, but not merely sentiment or headlines. That’s likely why CNN and CNBC partnered with Kalshi. News is filtered through editors, incentives, and bias. Taking headlines at face value is not a winning trading strategy. Savvy traders treat news as one input among many variables. If a headline says “Poll X shows Clinton up 10 points,” markets may adjust, but they don’t blindly price the headline. They factor in other variables. I’d argue markets are often smarter than the news. Domer❤️‍🔥 has even argued that Fed markets on Kalshi are more accurate than CME due to traders like himself making them more efficient, and I think he’s right. 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” Merriam-Webster defines insider information as “information not known to the public that one has obtained by virtue of being an insider.” You can obtain non-news information without being an insider. This overlaps with point one, but here’s a concrete example. For the recent TN-07 special election, I traveled to TN-07 and spoke with voters leaving early-voting sites and with everyday residents. I learned how little awareness there was that a special election was even happening, and how voters were thinking about the race. That information wasn’t in the news, but it informed how I traded. I’m not an insider. This was “alpha hunting” through firsthand observation. Almost every serious prediction market trader has similar stories. This is certainly not "insider trading." I’m genuinely curious to hear your thoughts, Coffeezilla, and hope for a good-faith dialogue. I hope you are doing well!

Benjamin Freeman

80,021 Aufrufe • vor 8 Monaten

This trader built a trading bot with Claude and made $223,000 on Polymarket His bot makes 113 trades per hour and trades the microstructure of short crypto Up/Down markets with a median size of $2.12 His strategy is simple: > Trades outcomes at 98-99.9c when the result is already obvious, but there is still edge left on Polymarket > Buys cheap tails at 1-5c in case the underlying asset moves in the final seconds > Sometimes builds arbitrage positions when it sees edge This guy’s Polymarket account: With this strategy and clean execution, he generates consistent profit and keeps growing his deposit

This trader built a trading bot with Claude and made $223,000 on Polymarket His bot makes 113 trades per hour and trades the microstructure of short crypto Up/Down markets with a median size of $2.12 His strategy is simple: > Trades outcomes at 98-99.9c when the result is already obvious, but there is still edge left on Polymarket > Buys cheap tails at 1-5c in case the underlying asset moves in the final seconds > Sometimes builds arbitrage positions when it sees edge This guy’s Polymarket account: With this strategy and clean execution, he generates consistent profit and keeps growing his deposit

VALIX

40,816 Aufrufe • vor 2 Monaten

This trader turned $76 into +$4,304 with a single bet on the weather market! Account: His PnL in March: -$1,000. His PnL now: +$12,000. Best trades: $76 -> $4,304 $19 -> $1,370 $52 -> $1,016 Main strategy: buying low-probability outcomes. This approach can lead to 50 losses in a row, but weather markets are full of inefficiencies. That’s why he sticks to his strategy, and in the end it pays off. With this approach, win rate doesn’t matter. One successful 1¢ trade can generate huge profit even if the previous 49 trades were losses. Simple math. The lower the probability of the outcome, the smaller his position size. Perfect use of the Kelly Criterion allows him to avoid going negative from incorrect sizing. Result: +$10,000 in just one month! To profit from this strategy, just set up copy trading on his account:

This trader turned $76 into +$4,304 with a single bet on the weather market! Account: His PnL in March: -$1,000. His PnL now: +$12,000. Best trades: $76 -> $4,304 $19 -> $1,370 $52 -> $1,016 Main strategy: buying low-probability outcomes. This approach can lead to 50 losses in a row, but weather markets are full of inefficiencies. That’s why he sticks to his strategy, and in the end it pays off. With this approach, win rate doesn’t matter. One successful 1¢ trade can generate huge profit even if the previous 49 trades were losses. Simple math. The lower the probability of the outcome, the smaller his position size. Perfect use of the Kelly Criterion allows him to avoid going negative from incorrect sizing. Result: +$10,000 in just one month! To profit from this strategy, just set up copy trading on his account:

me_cool_off

12,213 Aufrufe • vor 3 Monaten

This Polymarket looks like an easy bond - 'Will $BTC be above $100k on Nov 13?' - Current $BTC price is $103k - Odds are 95.5% - There's 10 hours left for the market to resolve - IMO this is an easy bond Smart Money 1) Heavily skewed towards YES 2) This trader - blubblerbuster consistently trades Price Prediction Markets successfully and has a PnL of $27k 3) He's the 3rd biggest holder of YES (Pro Tip: Here's how I found this market on TradeFox: a) Open the Market Dropdown on the trade page b) Use the Bonding Soon filter c) Click on interesting markets ) Link in comments

This Polymarket looks like an easy bond - 'Will $BTC be above $100k on Nov 13?' - Current $BTC price is $103k - Odds are 95.5% - There's 10 hours left for the market to resolve - IMO this is an easy bond Smart Money 1) Heavily skewed towards YES 2) This trader - blubblerbuster consistently trades Price Prediction Markets successfully and has a PnL of $27k 3) He's the 3rd biggest holder of YES (Pro Tip: Here's how I found this market on TradeFox: a) Open the Market Dropdown on the trade page b) Use the Bonding Soon filter c) Click on interesting markets ) Link in comments

Yoshi

18,123 Aufrufe • vor 9 Monaten

Hermes AI trading agent + math from this article = $440,000 profit on Polymarket in just 20 days. Zero profile views. This guy made almost $500k and everyone is acting like it’s completely normal. 85% win rate = he’s not guessing the future, he’s farming pure mathematical edge. Wallet of the agent: 850 green trades out of every 1,000. At his size and ROI he makes 11x more on winners than he loses. This is the real secret of any top PnL trader on Polymarket: stop guessing and and just exploit the math. Save this post so you don’t forget to read the article. Or take action right now and copy every trade this agent makes using this TG bot:

cvxv666

59,552 Aufrufe • vor 3 Monaten

INSIDE JUPITER: TRADING THE NEWS WITH PREDICTION MARKETS This episode with Julian breaks down why prediction markets are becoming a core primitive of Web4, how the Polymarket partnership fits the Jupiter full-stack vision, and why the future isn’t a separate product it’s prediction embedded everywhere. time stamps : 00:00 Intro Inside Jupiter HQ 00:18 Who's Julian and what's his role 00:44 What JuPredict actually is: “trading the news” 01:11 Polymarket × Jupiter 02:01 Why prediction markets are emerging 03:00 Markets vs commentators 04:24 Local & social prediction markets 05:15 On-chain transparency & global accessibility 06:27 Prediction markets in the Jupiter Web4 stack 07:48 The vision: predicting from any interface 09:28 Why people misread market prices 10:20 The real edge: studying trade flow before events 11:05 What prediction markets are best at predicting 11:40 Consistency & accuracy of the markets 12:10 Message to the community

INSIDE JUPITER: TRADING THE NEWS WITH PREDICTION MARKETS This episode with Julian breaks down why prediction markets are becoming a core primitive of Web4, how the Polymarket partnership fits the Jupiter full-stack vision, and why the future isn’t a separate product it’s prediction embedded everywhere. time stamps : 00:00 Intro Inside Jupiter HQ 00:18 Who's Julian and what's his role 00:44 What JuPredict actually is: “trading the news” 01:11 Polymarket × Jupiter 02:01 Why prediction markets are emerging 03:00 Markets vs commentators 04:24 Local & social prediction markets 05:15 On-chain transparency & global accessibility 06:27 Prediction markets in the Jupiter Web4 stack 07:48 The vision: predicting from any interface 09:28 Why people misread market prices 10:20 The real edge: studying trade flow before events 11:05 What prediction markets are best at predicting 11:40 Consistency & accuracy of the markets 12:10 Message to the community

JUP AND JUICE (🧃)

20,990 Aufrufe • vor 5 Monaten