Penny Lane

I’m a stock trader and love what I do. Father. Architect. 🇺🇸🇮🇱🇮🇹

[Steve Burns @SJosephBurns]

Hey @grok

Explain the strategies Kristjan Kullamägi (known online as Qullamaggie) used to grow a small account of roughly $5,000 into more than $100 million in trading profits within roughly a decade.

[Miles Deutscher @milesdeutscher]

This is my favorite article of the past three months.

It's honestly one you just have to read for yourself to see how good it is.

How to ACTUALLY be profitable with AI trading bots (no BS guide):

[Perplexity @perplexity_ai]

Perplexity Computer now creates interactive charts and visualizations directly in your thread.

For financial data, Computer uses @tradingview Lightweight Charts for candlesticks, volume, and moving averages.

[Steve Burns @SJosephBurns]

https://t.co/rfc6DVXy8n

[silvan @silvanrec]

CLAUDE AHORA OPERA MI TRADINGVIEW SOLO

NO TOQUÉ EL RATÓN NI UNA VEZ

Le pedí:

"Encuéntrame todos los futuros de BTC con RSI por debajo de 30 y volumen +200% a la vez"

14 segundos después:
→ 6 contratos encontrados
→ Gráficos cargados automáticamente
→ Soportes dibujados
→ Script de Pine corriendo backtest
→ Zonas de entrada marcadas

Luego: "Reproduce la semana pasada y enséñame dónde habría entrado mi sistema"

Pasó solo a modo replay. Vela por vela, marcando cada entrada y calculando PnL en directo.

9 operaciones. +4.780$ teóricos en el backtest. 83% de acierto (en esa prueba, ojo, backtest no es real).

También le pido indicadores:
"Escríbeme un oscilador de momentum que cruce actividad de ballenas con tendencia de precio"

40 segundos. Script listo, indicador en el gráfico.

La mayoría sigue pasando 50 gráficos a mano.

Esto barre 200+ en un minuto.
Escanea, dibuja, testea, calcula.

No reemplaza tu estrategia.

La ejecuta mucho más rápido.

El tutorial del post de abajo te enseña cómo montar tu propio bot de trading

[Perplexity @perplexity_ai]

Introducing Brain in Computer.

Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain.

It makes Computer more stateful with every run.

Available as a research preview for all Perplexity Max subscribers.

[Penny Lane @PennyLaneUS]

@codewithimanshu Money

[Himanshu Kumar @codewithimanshu]

$205 → $2,582 Overnight.

I Told Claude to Scan Every Polymarket Wallet While I Slept. Woke Up to the Terminal Still Running.

87% of Polymarket is exit liquidity. You're it. My 6 Claude agents aren't.

I have full step-by-step guide to replicate this entire Claude scanning + copy trading setup.

Giving This Free for 24 hours. To get it:

1. Comment the word "Money" & Like and Retweet this post.

2. Follow me @codewithimanshu ( then only, i can DM you)

That's the whole system for you to start making money online using ai & polymarket.

One prompt before bed:

"Scan every Polymarket wallet and GitHub repo. Find asymmetric payouts."

Woke up to +$2,582. Terminal still running.

Claude analyzed thousands of wallets overnight. Most of them bleed money. But 340 had something different.

Asymmetric payouts:

> Enter at 27 cents. Exit at 91.
> Lose 27 cents when wrong. Win 64 when right.
> Only need to be right 1 in 4 times.
> They were right half.

You need a 25% win rate to profit. They hit 50%. That's not luck. That's edge.

Claude found 3 patterns:

Category specialists. One wallet: 91% win rate on crypto. 14% on politics. Total looks average. Filter to crypto only and it's a top performer. You'd never find this manually.

Speed arbitrage. 47 wallets entering 3-8 seconds after Binance moved. Before Polymarket updated. Not predicting anything. Just faster than you.

Near-zero accumulation. Buying at 2-8 cents weeks before resolution. $8 in. $200 out. Hundreds of times. While you scroll past markets priced at 3 cents thinking "that'll never hit."

It hits. And these wallets are there every time.

Claude connected GitHub repos to run all three:

> poly_data: scanned every wallet and entry price
> polyterm: whale tracking and insider detection
> py-clob: placed the orders

7 wallets copied. 204 trades. $25 seed.

6 agents running right now:

> sigma_decay: 78% conviction
> darkpool_7: 87% conviction
> velvet_void: 85% conviction
> profitprinter: 54% conviction

Equity curve: $25 to $$12,582. No meaningful dip. Sharpe 2.09.

You're looking at Polymarket with your eyes. Claude is looking at it with thousands of wallets of data, GitHub repos, and pattern recognition running in parallel while you sleep.

You're not even playing the same game.

You Must Follow me @codewithimanshu, so i can send you DM.

[Himanshu Kumar @codewithimanshu]

Jim Simons turned $100 into $130 billion using math.

He just gave the entire playbook in a free 1-hour MIT lecture.

You've been picking stocks based on Reddit and vibes.

He returned 66% per year for 30 years using equations.

This is the most valuable hour you'll spend this week.

Save the video. Watch it tonight. Build the bot this weekend. Follow
@codewithimanshu

for more high-signal content that turns lectures into income.

↓

Below what nobody is telling you about this lecture.

Everything Jim Simons taught Renaissance Technologies in the 1980s is now buildable in a weekend with Claude Code.

Pattern recognition across thousands of assets. Signal detection in noise. Automated execution.

Risk management at scale. In 1988, this required a team of 50 PhDs and millions in infrastructure.

In 2026, one person with Claude + a laptop can build a working version in 7 days.

The knowledge gap between you and a Renaissance trader is now smaller than it has ever been in history. Follow

Follow
@codewithimanshu
for daily breakdowns of the AI tools that turn this knowledge into income.

↓

What this lecture actually teaches you to build.

Simons walks through the core principles that printed $130 billion:

> Find statistical edges that are invisible to humans
> Trade only when the math says yes, never on emotion
> Run hundreds of small bets simultaneously, not one big bet
> Cut losses ruthlessly when signals weaken
> Compound relentlessly across decades These aren't trading tips.

These are the foundational principles of every AI trading bot worth running. Watch the lecture. Take notes. Then turn it into a system. Follow
@codewithimanshu

for the exact Claude prompts that turn quant theory into deployed bots.

↓

Your weekend playbook to turn this into profits.

Friday night: watch the Simons lecture. Take notes on every signal he mentions.

Saturday: open Claude Code. Build a backtesting framework using historical price data.

Test 3-5 of his core signal ideas.

Sunday: paper trade your best signals on Polymarket, Toobit, or Alpaca. Validate before risking real capital.

Monday: deploy a small position. $100. $500. Whatever you can lose without flinching.

Compound. Iterate. Scale.

That's how a Simons-grade trading system gets built in 2026. Not over 30 years. Over one weekend. Follow
@codewithimanshu

for the exact templates and prompts to build each piece.

↓

for more high-signal content that turns lectures into income.his is the best time in history to build wealth with AI trading.

Jim Simons needed:

> A team of 50 PhDs
> $25 million in compute
> 10 years of infrastructure
> Custom data feeds nobody else had

You need:
> Claude Code
> A laptop
> 7 days of focus
> $20/month in API costs

Same math. Same principles. Same edge.

Different barrier to entry.

People who watch this lecture and build with the knowledge will compound for the next decade.

People who save it for later, will still be picking stocks based on vibes in 2027.

Save the video. Watch it tonight. Build the bot this weekend. Follow @codewithimanshu

[cvxv666 @antpalkin]

ChatGPT can write you a thousand trading strategies from one sentence.

999 will quietly bleed your account dry. Every single one of them looks like a winner.

One method pulls the real one out. Quant desks have run it for years. It's called loop engineering.

It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen.

Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week.

And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live.

> describe an idea -> backtest 5 years in 12 seconds
> read why it died -> feed that back in
> run it again until one survives data it never saw

AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real.

89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop.

Horizon just put the whole thing in a chat box, free while the beta's open: https://t.co/qMCIUPVKGK

You're not losing to smarter traders. You're losing to the ones who opened the tab.

Anyone can generate a strategy now. Almost no one runs the loop that proves it.

That gap is the whole edge.

You'll mean to check it later. Later is a "waitlist closed" page.

[Steve Burns @SJosephBurns]

Late billionaire trader Jim Simons explains alpha:

[ₕₐₘₚₜₒₙ @hamptonism]

Perplexity just became the the first Al company to truly go head-to-head with the Bloomberg Terminal...

Using Perplexity Computer (with no local setup or single LLM limitation), it was able to build me a terminal with real-time data to analyze $NVDA using Perplexity Finance:

[Eyad @eyad_khrais]

https://t.co/i5L6UIPgH8

[Quant Science @quantscience_]

Algorithmic trading is the domain of secretive Hedge Funds.

Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool).

Here are 17 Python libraries that open the hedge fund black box (📚bookmark this for later):

[Ricardo @Ric_RTP]

Elon Musk just confirmed the most INSANE IPO in history.

SpaceX is going public in 2026.

$1.5 TRILLION valuation. Raising $30+ billion.

That's the biggest IPO ever made. Beating Saudi Aramco's $29 billion record from 2019.

But here's what everyone's missing:

This isn't about space tourism or Mars missions.

Elon is literally about to win the entire AI race.

And 99% of people have no idea how...

Here's the problem killing every AI company right now:

POWER.

Oracle just reported earnings.

They burned through $12 BILLION in one quarter building data centers.

Their free cash flow? NEGATIVE $10 billion.

Revenue missed estimates. Stock crashed 11%.

Microsoft, Amazon, Google all scrambling to find enough electricity for AI training.

The brutal math:

The US generates 490 gigawatts of total power.

AI is projected to need 123 gigawatts by 2035.

That's a QUARTER of the entire electrical grid. Just for artificial intelligence.

Goldman Sachs says AI energy demand could jump 165% by 2030.

There is literally not enough power on Earth to run AI at the scale these companies are promising.

Every data center needs massive cooling systems. Billions of gallons of water per year. Insane energy costs.

And the infrastructure can't keep up.

Elon's solution?

Stop building on Earth entirely.

SpaceX is building data centers in SPACE.

Not a concept. Not 10 years out. Literally starting in 2026.

They're upgrading Starlink V3 satellites to carry AI computing chips.

Each satellite gets 24/7 solar power. No clouds. No night. No weather disruptions. No grid bottlenecks.

And the insane part is that Starship can deliver 300 to 500 gigawatts of solar-powered AI satellites into orbit every single year.

At 300 gigawatts per year, the AI computing power in space would exceed the entire U.S. economy's total electricity consumption within two years.

Just from satellites. Processing in orbit.

While Oracle is begging banks for loans to finish data centers and OpenAI is stuck in circular funding arrangements with Microsoft, Elon already owns everything:

The rockets. The satellites. The launch infrastructure. The AI company (xAI).

He doesn't need to ask utilities for permission.

Doesn't need grid approvals from local governments.

Doesn't need to build nuclear plants or wait for clean energy.

He just launches.

And everyone else is scrambling to catch up:

Jeff Bezos sees it. Blue Origin announced they're building their own orbital data centers.

Google just launched "Project Suncatcher" with plans to deploy AI satellites by 2027.

Eric Schmidt, the former CEO of Google, literally BOUGHT an entire rocket company (Relativity Space) just to compete in this space.

But they're all 3+ years behind Elon.

SpaceX already has 6,000+ Starlink satellites in orbit. The infrastructure is built.

The $30 billion from the IPO?

Going straight into scaling orbital compute.

SpaceX revenue is jumping from $15 billion in 2025 to $24 billion in 2026.

Most of that from Starlink. Now add space-based AI infrastructure on top.

Here's why this matters:

Whoever controls orbital computing controls the AI revolution.

And there's only ONE company on Earth with fully reusable rockets that can launch at the scale required.

Jensen Huang, Nvidia's CEO, called space data centers "a dream."

Translation: Nvidia is screwed if Elon actually pulls this off.

Because if SpaceX succeeds, every AI company on the planet becomes Elon's customer.

OpenAI needs compute? Running on SpaceX satellites.

Google needs more capacity? Renting orbital infrastructure.

Microsoft needs power? Paying SpaceX for launch and compute access.

Elon won't just be in the AI race.

He'll own the entire track everyone else is running on.

The $1.5 trillion valuation sounds crazy until you realize what he's actually building.

It's not a rocket company. It's the infrastructure layer for the next 50 years of computing.

People calling it overvalued have no idea what's coming.

[Penny Lane @PennyLaneUS]

@SJosephBurns Team 5

[Penny Lane @PennyLaneUS]

@SJosephBurns Team 3. Easy

[Quant Science @quantscience_]

How to make a simple algorithmic trading strategy with a 472% return using Python.

A thread. 🧵

[Baptiste @BaptisteVicini]

In 2014, Elon "gave away" Tesla's secrets to BWM.

Everyone thought he was crazy.

But this "act of charity" was actually the most ruthless business move in corporate history.

Here's why BMW never saw it coming: 🧵