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Chase
514 posts
Chase
@0xChaseTM
pushin P (@Polymarket) | @zscdao member | dm open
Cyprus เข้าร่วม Kasım 2024
75 กำลังติดตาม403 ผู้ติดตาม
This lecture is the simplest systematic edge ever published - and the same framework runs on Polymarket today
Greenblatt's formula turned $10K into $1M while the S&P barely cleared $73K over 16 years
Bookmark this - the math is so simple it almost feels illegal
Here's the formula:
Combined Rank = Rank(ROC) + Rank(Earnings Yield)
ROC = how good the business is
Earnings Yield = how cheap the stock is
Rank ~500 companies on both. Sum the ranks. Take the top 30. Hold 12 months
The results:
30.8% annual returns. 16 years
$10K → $1M S&P same period: $10K → $73K
Worst year: -4%. Best year: +79.9%
When S&P lost 43% over 3 years → formula returned +73.5%
Why almost nobody runs it:
5 months a year it underperforms. sometimes whole years
Fund managers can't survive that - clients pull capital quarterly
The edge belongs to retail with long horizon and discipline
The Polymarket parallel:
Greenblatt: rank stocks on ROC + Earnings Yield → take top 30
Polymarket quants: rank buckets on ECMWF + GFS divergence → enter biggest gaps
Same brain. Different table
→ rank systematically. no opinions
→ never override the model
→ accept some periods underperform
→ let small consistent edges compound
The key insight:
Greenblatt didn't beat the market with intelligence
He beat it with a 2-metric formula and the discipline to follow it
Polymarket isn't different
The math is public. The discipline is rare
Watch the lecture below ↓
Chase@0xChaseTM
This Polymarket trader has 2,668 trades, 99% win rate, and skipped every loud market on the platform It sounds impossible until you see how simple the math behind it is ECMWF and GFS forecasts vs a casual order book - the gap is wide and compounds daily Here's the system: He doesn't predict weather He prices against it. ECMWF and GFS already know the answer - he just acts on it. The niche: Toronto. Taipei. Tokyo. Manila. Karachi. Singapore. Jeddah. Lagos. Paris. Munich. NYC. Chicago. Denver. Daily temperature binaries. Nothing else Three entry patterns: → Buy No at 85¢–99¢ on exact-degree questions "will Madrid hit exactly 16°C" - hitting one specific degree is rare. No wins almost every time Example: Taipei 29°C No at 85.3¢ → +$438 → Buy Yes at 95–98¢ on threshold questions when models are confident "will Karachi hit ≥41°C" - when forecast clears it cleanly Example: Karachi ≥41°C at 98¢ → $7,230 ticket → +$137 → Buy 3¢ lottery tail bets on rare-but-possible outcomes Example: Paris 22°C → $9 invested → $260 payout → +2,766% The math: Ticket sizes: $1K–$7K Profit per trade: 2–15% Hold time: 1–3 days Cities traded daily: 20+ Active position value right now: $108 Same capital recycled across dozens of cities every single day Why it works: ECMWF and GFS produce sharper probabilities than the Polymarket order book at 24–72h horizons. The book is full of casual users He's pricing against actual physics The risk profile: Each trade has convex downside - lose 100% if wrong Diversified across 20+ uncorrelated geographies A freak weather day in one city barely dents the book The lottery tail bets are the asymmetric upside layer The key insight: The loud markets are where attention lives The quiet ones are where the math wins He's not smarter than the market He's just running a public model against a casual order book The atmosphere is the only thing that can beat him And it almost never does
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@0xChaseTM Greenblatt's formula is a testament to the power of systematic investing, delivering remarkable returns with simplicity.
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@de1lymoon From TV Forecast to professional trading, he knows this niche so well
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A former TV weatherman quit his job and turned $505 into $10,100 on a Karachi temperature bet.
$125,696 P&L. 2,592 predictions. Same edge. Same 1,900%
JoeTheMeteorologist used to read forecasts on television. Now he reads them on Polymarket
How it works:
you can do all of this on → predictparity.com/?code=moon
He doesn't need a model. He has a degree in meteorology and 15 years of reading weather charts
When the market prices a bucket at 5¢, his trained eye sees ~14% probability. That gap is the trade
Every winning entry: 5¢. Every winning ROI: 1,900%. The math is identical because the edge is identical
His winning trades all sit in the same pattern:
$1,944 → $38,892 (Paris 19°C YES at 5¢, +1,900%) $650 → $13,002 (Ankara 14°C YES at 5¢, +1,900%) $505 → $10,100 (Karachi 34°C YES at 5¢, +1,900%)
A weatherman with no bot. No code. No quant degree. Just the same skill he used on TV applied to a market that pays him for being right
The job was reading the sky. The market never changed
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@0xChaseTM I watched the entire video - it's very informative, thank you
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Polymarket user made $16.7K on weather markets in just 45 days
This dude is printing money by betting on weather all over the world every single day:
+$244 - today
+$6.9K - past week
+$15.2K - past month
He’s flipping pocket change into massive gains:
$16.64 -> $4082
$28.57 -> $2346
$42.52 -> $1059
Specific ranges don't matter to him, he trades across all odds
Position size is almost always under $100
With a 64.5% win rate over 6548 predictions, he’s a rising star in weather markets
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@Damir_Akaza Of course, broski
Morning coffee and this video will help you start the week off right!
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@0xChaseTM These smart complicated things definitely shouldn’t be read on the last day of the weekend... I’ll save it for tomorrow)
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BRYAN JOHNSON ANALYZED HIS GIRLFRIEND'S VAGINA. NOW HE'S ANALYZING HIS BALLS.
This man operates on a different level entirely.
April 30, two minutes after giving his girlfriend oral sex, he posted her vaginal microbiome report to his million followers.
→ 100/100 score, top 1% of all vaginas
→ 98.7% dominated by Lactobacillus crispatus - the most protective species a vagina can host
→ no STIs, no bad bacteria, the lab found absolutely nothing wrong
→ post gets 21 million views
Internet reacts: "dude charts his girlfriend's p**sy instead of the stock market"
Then last week he drops the balls update.
100% of men have microplastics in their semen right now. Every single one. Bryan had 165 particles/mL in November 2024.
18 months of daily saunas, reverse osmosis water, and removing every plastic item from his life later.
→ November 2024: 165 particles/mL
→ July 2025: 20 particles/mL
→ April 2026: 0 particles/mL
Potentially the first human being alive to achieve this.
Zero microplastics in his balls.
Top 1% vagina in his bed.
Posting about both at midnight.
The rest of us are just trying to drink enough water.
New York Post@nypost
Bryan Johnson boasts about health achievement for his balls: 'This should not be possible' trib.al/GoZQLuj
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@0xChaseTM The idea of markets as ecosystems is truly thought-provoking.
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This Oxford lecture explains why every quant fund beats Polymarket retail traders
The framework Citadel, Jane Street and Renaissance use - explained by the MIT professor who built it
Bookmark this - 60 min that change how you think about markets forever
Here's the principle:
Andrew Lo didn't accept the Efficient Markets Hypothesis
He replaced it with something that actually describes how markets behave
Markets aren't physics. They're biology.
Not equations. Ecosystems
The shift:
→ Classical economics: rational agent, equilibrium, optimization
→ Reality: 1998 LTCM. 2007 quant crisis. 2008 crash.
→ Behavioral economics added bias names. Lo built a unified theory
The central insight:
Market participants behave the way they do because of evolution
Optimized for survival in the savanna - not for trading on Polymarket
"Irrationality" is often rational in evolutionary terms - wrong context
The probability matching example:
Coin shows heads 75% of the time.
Rational play: bet heads every time.
What humans actually do: bet 75/25 split.
Looks irrational. Costs expected value.
But bees do it. Pigeons do it. Fish do it.
Too consistent across species to be a bug
Lo's evolutionary model:
When outcomes are independent across individuals → maximize expected value
When outcomes are correlated → randomize. probability match. survive
What looks like a bug is group insurance against systemic shocks
"Irrational" individually = optimal at the species level
Why this matters for Polymarket:
→ retail crowds react to headlines - that's the savanna brain firing
→ quant funds price the ecosystem - not the rational agent
→ "irrational" prices aren't noise - they're the species protecting itself
→ the edge: read the ecosystem, not the equation
The hedge fund parallel:
Lo calls hedge funds "the canary in the coal mine"
They adapt fastest. They signal systemic risk first
Same logic on Polymarket - quants adapt to mispricing before retail catches up
The key insight:
Retail trades the chart
Quants trade the ecosystem
One keeps winning. One keeps losing
Watch the lecture below ↓
zostaff@zostaff
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Prague 🇨🇿 hitting different today 🖤
Managed to swing by @EthPrague and finally link up with my guy @XJustmichael in person - energy was immaculate!
Massive shoutout to him and the whole @Trezor team for the fire gift 🎁
Moments and real connections like this are the real alpha in this space ♥️
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This trader made +$566,000 in just one day on a Russia-Ukraine ceasefire!
Account: @wan123?tab=positions?via=me_cool_off" target="_blank" rel="nofollow noopener">polymarket.com/@wan123?tab=po…
Total PnL: +$1,056,000.
Only 277 trades!
After Trump announced a 3-day ceasefire, he bought YES on two dates:
> May 31 for $38,000 at 11.3¢
> June 30 for $25,000 at 8.7¢
As a result, he turned $63,000 into +$630,000 in just one day!
Net profit: +$566,000!
This trader clearly understands geopolitics - he previously made his fortune on the Iran-US conflict.
To copy the trades of this geopolitical guru use the TG bot: @mecooloff" target="_blank" rel="nofollow noopener">kreo.app/@mecooloff
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i spent 40 minutes explaining to Claude who i am.
my niche. my voice. my audience. what i never say.
generic output. every single time.
turns out the problem wasn't Claude.
it was me giving it nothing to work with.
the fix: a knowledge layer.
20 minutes to set up. your notes, tweets, articles dumped into one folder.
agent reads it overnight and builds 200+ wiki pages from your own thinking.
next morning your AI finally sounds like you.
90% of people skip this. they just prompt → accept → ship. and wonder why everything sounds the same.
and if you learn to set this up for others?
10 clients = $56,800/year.
full guide below 👇
Shann³@shannholmberg
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How a secretive group of 3,500 math nerds made $39,000,000,000 last year without predicting the market.
Jane Street doesn’t care if stocks go up or down. They are a pure math factory. While retail traders gamble on direction, Jane Street uses complex algorithms and high-frequency arbitrage to extract billions from market inefficiencies in milliseconds.
They defeated JP Morgan and Goldman Sachs not with money, but with code.
Bookmark & watch Bloomberg expose the most profitable and secretive firm on Wall Street. Then read the post below to see a live whale using this exact algorithmic logic to print $200,000 on Polymarket right now
ludoonchart@ludoonchart
How a math PhD built a $10,000,000,000 empire with exactly ZERO human traders. Alexander Gerko founded XTX Markets. They trade $300 Billion every single day. No charts, no financial news, no stress. Just pure machine learning predicting price action. While retail traders draw support lines and panic, his automated models systematically extract their liquidity. Bookmark & watch Bloomberg break down his "no humans" strategy. Then read below to see exactly how these quantitative systems are built
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@BimbaCrypto 21,5k predictions in two month. The chart looks well. this bot works as it should be
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🧠I FOUND A CLAUDE BOT ON POLYMARKET: 21,556 TRADES, $546K PROFIT - GRINDING 5‑MIN BTC WINDOWS
Meet 0xB27BC9. Joined March 2026. 5.7K views.
> 21,556 predictions in ~2 months = ~400 trades/day > All‑time profit: $546,887
> Only trades 5‑minute Bitcoin “Up or Down”
HIS BIGGEST WINS
> Down @ 49.1¢ → 36,318 shares → +$18,478 (+103%)
> Down @ 8.2¢ → 38,064 shares → +$11,898 (+382%)
> Down @ 48.1¢ → 22,178 shares → +$11,502 (+107%)
> Up @ 26.7¢ → 40,010 shares → +$10,966 (+102%)
> Up @ 1¢ → 9,930 shares → +$6,752 (+6,800%)
WHY THIS IS A CLAUDE BOT
> 400+ trades per day - inhuman consistency
> Buys at 1-50¢, never chases 99¢
> Wins 100-6,800% when right, loses pennies when wrong
> Zero emotion, perfect repeatability across thousands of windows
THE STRATEGY
> Buys directional 5-min BTC positions at 1-50¢
> Wins 2-68× when right (especially on sub-10¢ entries) > Scales size up to 40k shares per trade
21,556 trades. $546k in two months.
Claude bot just printing.
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Every Wall Street quant started with this lecture - Andrew Lo opening MIT's Finance Theory course
He founded AlphaSimplex Group in 1999 and sold it to Natixis 8 years later, then cofounded BridgeBio Pharma - now a publicly traded biotech with a multi-billion dollar market cap
Director of MIT's Laboratory for Financial Engineering, Guggenheim Fellow, Sloan Fellow, awarded the Paul A. Samuelson prize for his work on hedge fund risk
The article above is what comes after you finish his course - KL-divergence, max-entropy, Kelly sizing, three tools you can apply to Polymarket today
Bookmark it & give it a watch tonight ↓
0xDipper@Dipper_pol
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A weather sniper turned $13 into $4,082 just by betting on the weather
Bought YES at 0.2c that the highest temperature in Madrid would be ≤17°C
The next day his balance grew by $4,000
His best bets over the last few days:
$5 → $500 (9,900%) - Hong Kong 27°C April 11
$28 → $2,346 (8,112%) - Seattle 61°F or below May 6
$10 → $653 (5,881%) - Denver 52-53°F April 26
$5 → $251 (4,900%) - Chicago 74-75°F April 16
$17 → $626 (3,474%) - Buenos Aires 25°C April 23
6,350 bets last month
His profile: @ramsbr?tab=positions" target="_blank" rel="nofollow noopener">polymarket.com/@ramsbr?tab=po…
His bot scans weather forecasts around the world
Enters when the market price is lower than what the forecast says
He mainly bets on weather markets $15,000+ profit last month
+$5,905 in the last week alone
0xRicker@0xRicker
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i think half my phone gallery is just gym
this is my favorite pic
Julia OG@yuliiaun1204
i think half my phone gallery is just gym this is my favorite pic
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@0xChaseTM Timeless insights from Jim Simons, still highly relevant today, a must-watch for traders and quants.
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This 44 min lecture is the framework Polymarket quants run every single day
Jim Simons explains how Renaissance Technologies built the most profitable fund in history
Bookmark and watch before your next trade - the math hasn't changed in 40 years
Here's why:
Jim Simons didn't trade like Wall Street
He hired mathematicians, physicists, astronomers - not a single MBA
Renaissance Technologies. Medallion Fund. The most profitable fund in history
The five principles behind everything Simons built:
→ Only data, never opinions - trade only on signals from historical price series. fundamental analysis: never used
→ Scientists over financiers - pattern recognition is a science problem, not a finance problem. unbiased minds find what others can't
→ Predictions, not explanations - it doesn't matter why the market moves. it matters that it moves predictably from past data
→ Diversify your signals - many independent weak predictions = stronger result. it's a portfolio of hypotheses.
→ Constantly review the models - patterns come and go. what worked last year may not work today
Three personal principles Simons lives by:
→ Be guided by beauty - a beautiful theorem and a well-built business have the same nature
→ Hire smart people - power of mind beats experience in any field
→ Don't listen to experts - bright outsiders with open minds beat insiders stuck in their views
The same principles on Polymarket today:
→ ECMWF and GFS = data, not opinions
→ price signals from 20+ cities = portfolio of independent weak predictions
→ enter when models converge - exit at resolution
→ scientists trade weather. casuals trade headlines
→ models drift — review them constantly
The key insight:
Renaissance didn't have magic
It had a framework
Same framework runs Polymarket today
The barrier was never intelligence
It was access to the data and the discipline to follow the model
Both are now public
Watch the lecture and read the article below ↓
Roan@RohOnChain
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