Ex-Citibank quant trader built Polymarket bot and turned $1,708 → $83,864 in 60 days using one secret
I traced his 30,280 trades using Claude → backtested his strategy on 78M trades dataset
the secret is running two engines simultaneously. one hunts deep discounts, one fades the crowd on high-conviction windows.
engine 1 - deep discount
finds contracts priced below 10¢ on BTC, ETH, SOL, XRP windows. market says less than 10% chance. model says closer to 50%. enters small, $8-$100 per trade
engine 2 - high conviction fade
finds windows where crowd is slightly overpriced on the wrong side. enters with $1,000-$1,200 per trade at 50-57¢
engine 1 finds the outliers. engine 2 grinds consistent returns
Profile: https://t.co/7ieyYGDjka
THIS COMPLETELY CHANGES THE GAME
a ready-made Claude Code playbook straight from its creator
if I'd seen this six months ago, I'd be shipping way faster by now
in the hands of someone who knows how to use it, this rewrites your whole dev process for good
This 20-min workshop by the creator of Claude Code will teach you much more about vibe-coding than 500 YouTube video guides and tips
Bookmark it & give it 27 minutes today. This video will change the way you use Claude forever
This trader made $1,113 by betting $56 on the Amsterdam temperature using one pattern
He doesn't trade one market. He trades every market where the crowd is asleep at the wheel
he earned over $4,800 and has open positions for $9,700
The pattern is always the same: market prices something at 1–12¢. He knows it's happening. He enters. It resolves at 100¢
He doesn't specialise in a category. He specialises in finding markets priced at 2–12¢ where the answer is already obvious - and nobody has checked yet.
$56 → $1,113 · Amsterdam 15°C · Yes @ 5¢ · +1,888%
But that's not even his best trade
$161.50 → $3,521 · Elon Musk 300–319 tweets · Yes @ 1.7¢ · +2,080%
$188.69 → $1,791 · Elon Musk 65–89 tweets · Yes @ 5.6¢ · +849%
$72.17 → $1,078 · Toronto temp 18°C+ · Yes @ 5¢ · +1,394%
The edge isn't the market type. It's the price
Profile: https://t.co/9dWTqjli4q
This trader turned $80 into $2,732 with a single bet on the temperature in Shanghai
$20,969 last month and $42,105 all-time with 1,237 predictions
His model: GFS + ECMWF for weather. Public filings + hiring data for corporate
He pulls 3 independent weather models per city. When they all agree - he enters. When there's any doubt - he waits
The crowd prices Seoul 20°C at 25.9¢. The models already say it's happening. He loads
Market catches up at resolution. +210%
The 2.8¢ entry is the one that breaks your brain. $80 in. $2,732 out
The market gave 97% chance it won't happen. Three weather models said otherwise. He trusted the math
This is the same edge a meteorologist has over a tourist who checks the sky
Except he built a system that does it automatically. Across Seoul, Shanghai, Taipei, Shenzhen
That's not luck. That's reading a forecast 12 hours before the crowd does.
Profile: https://t.co/bh3jRWHYHz
Jane Street pays $650,000 a year to quants who trade using neural networks
This 1 hour lecture of a Jane Street talk on real ML infrastructure > 6 months of any course on YouTube
You'll finally see what tier-1 quants actually optimize
Bookmark & watch, no matter what
Andrej Karpathy - former OpenAI co-founder and ex-Tesla AI lead - just gave one of the most-watched AI talks of the year
If you build, write, or ship anything online - this 39-min talk reframes how the next decade plays out
bookmark - it will change the way you using AI tools
Two Sigma quants pull in ~$550K/year - and most of their edge comes from ML
This 1-hour deep-dive from Two Sigma's research team breaks down exactly how they apply machine learning to financial markets
Bookmark it and watch. Then read the article below
This HFT bot make $88,182/month with 0 profile views and a trade executed every 4.6 minutes
The market has no idea it exists
P(Xₙ₊₁ = j | Xₙ = i) = pᵢⱼ ≥ 0.87 → ENTER
That's the whole formula. Andrei Markov published it in 1906. It's been making this account $88K a month
He runs pure BTC directional scalping across hourly and 5-minute windows with a wide entry range
36¢–63¢, simultaneously holding Up and Down positions on different time windows at the same time
700–2,400 shares per position. Multiple windows open in parallel. One trade every 4.6 minutes, 24 hours a day
t detects when the Markov state is committed and enters regardless of direction. Both sides work. The math is identical either way
Mean entry ≈ 50¢ → avg r ≈ +100% per resolution
Profile: https://t.co/eJdGxZx8i9
Instead of watching Netflix, watch this 80-minute MIT lecture on the Black-Scholes model and risk-neutral valuation
You'll finally understand the risk-neutral measure and how a contract at 0.87 is literally a probability
Bookmark & watch, no matter what
82 minutes of MIT real analysis on logs, exp and power series > 6 months of quant crypto alpha YouTube slop
You'll finally understand what convergence and continuity actually mean - the stuff Shannon, Kelly and Thorp built their entire edge on
Bookmark & watch, no matter what
30 minutes of Veritasium's "The Equation That Beat Wall Street" > 6 months of any quant guru on YouTube
You'll finally understand what edge, information advantage and Kelly sizing actually mean and why Jane Street pays $650k a year for quants
Bookmark & watch, no matter what
This HFT bot prints $17–20K every week using one strategy and turned $215 → $199,214
$17,492 last week alone. 53,571 total predictions. Still going
The entry range is the widest of any bot I've tracked: 8.7¢ to 97¢
Two modes running at once:
Mode 1 - High-confidence scalping (81–97¢)
Enters when the market has already committed a direction but still leaves 3–19¢ on the table. Small spread, small size, extremely high win rate
Mode 2 - Extreme discount entries (8.7¢–57¢)
Fires when the Markov matrix shows a committed state that the market is pricing as near-impossible. One position at 8.7¢ is currently sitting at 9.5¢ - unrealized but loaded
Fastest way to copy-trade him even with $10 using: https://t.co/7qw0SmTB5j
350 trades/day × 30 days = 10,500 trades per month
Mean return per trade implied: $17,492 / ~2,200 weekly trades ≈ $7.95 net per prediction
The bot doesn't bet big on any single position - $1,309 biggest win confirms small sizing
Profile: https://t.co/PbNRbsXLWj
Does that remind you of anyone from @Polymarket?
83 minutes from Robert Gallager can explain you more > every betting-strategy YouTuber alive
Martingales are the mathematical language of every betting strategy since Doob - random walks, Wald's identity
Bookmark & watch. Then re-read the post above
This bot made $84,708 last month and 32k last week
Has 0 profile views. Nobody is watching
9,323 predictions. $17.6K biggest win. $117.7K deployed right now
P(Xₙ₊₁ = j | Xₙ = i) = pᵢⱼ ≥ 0.87 → ENTER
When that condition holds at 47¢, the edge is +112%. When it holds at 73¢, the edge is +37%
Mean entry across active positions ≈ 63¢ → avg r ≈ +58% per resolution
The formula works regardless of which direction the market has committed to
Fastest way to copy-trade him even with $10 using: https://t.co/7qw0SmTB5j
Pure BTC directional scalping across hourly and 5-minute windows
Entry zone 47¢–87¢ - the bot reads both sides simultaneously, betting Up and Down on different time windows at the same time.
$117.7K deployed × 58% avg return = $68K theoretical monthly yield on active capital
Profile: https://t.co/fYozC3ArpE
OpenAI's Head of Engineering just revealed how they actually use code agents in 2026
80 minutes. free. by the person who built it
watch it. bookmark it
95% of their engineers run 10-20 agents powered by Codex daily. you still type prompts one at a time
then read the article below
83 minutes of an MIT lecture on PCA in Finance > 6 months of any "quant trading" course on YouTube
You'll finally understand what factor models, eigenportfolios and covariance structure actually mean
Bookmark & watch, no matter what
This 80-minute MIT lecture on Time Series Analysis will teach you more about how prediction markets actually work than any trading course, financial institution or "alpha" group ever will
Bookmark & watch , no matter what. It'll be the most productive thing you do this week
This 1 hour MIT lecture will teach you more about Portfolio Management than 20 years inside any hedge fund, investment bank or financial institution
Bookmark & watch, no matter what. It'll be the most productive thing you do this week
Instead of watching an hour of Netflix, watch this 1-hour MIT lecture on "option price & probability duality" that will teach you more about options trading than a 2 month internship at Wall Street hedge fund
Bookmark this & watch, no matter what. It’ll be the most productive part of your day