Wealthy Bitcoin 🛸

🛸

[CyrilXBT @cyrilXBT]

CLAUDE CODE CAN NOW PULL LIVE DATA FROM 17,000+ STOCKS, CRYPTO PRICES, AND FINANCIAL STATEMENTS IN SECONDS.

One command. 60 seconds. Done.

Here is the exact setup:

Step 1: Open Claude Code and paste this:

claude mcp add --transport http financial-datasets https://t.co/cupUKrWK0C

Step 2: Authenticate
Type `/mcp` inside Claude Code and complete the OAuth flow in your browser.

Verify the connection anytime:

claude mcp list

Step 3: Start prompting
- "What is Apple's current P/E ratio and market cap?"
- "Show me Tesla's income statement for the last 4 quarters."
- "How has Bitcoin's price changed over the past year?"

That is it.

Claude Code now has direct access to real financial data across 17,000+ stocks, earnings reports, balance sheets, income statements, cash flow data, and crypto prices.

The analysts paying $24,000 a year for a Bloomberg Terminal are not going to be happy this exists.

Before this you needed a Bloomberg Terminal or a complex financial data API or hours of manual research across multiple sources.

Now you need one command and 60 seconds.

The quants, analysts, and portfolio managers who figure out how to combine Claude Code's reasoning with live financial data access will have a research edge that compounds every single day.

Bookmark this before you open your next brokerage account.

Docs if you run into errors: https://t.co/CgF6B3dS5V

Follow @cyrilXBT for every Claude Code integration that changes how you work with data.

[CyrilXBT @cyrilXBT]

A HONG KONG TRADER BUILT A BTC BOT THAT RUNS 10,000 SIMULATIONS BEFORE EVERY SINGLE TRADE.

He is not predicting the market.

He is running 10,000 versions of the future before the market moves.

You are picking trades on vibes.

He is running Monte Carlo simulations while you sleep.

Here is how the system works.

Monte Carlo simulation is the same mathematical framework quant funds like Renaissance Technologies have used for decades.

You take a potential trade.

You run 10,000 randomized versions of how that trade could play out based on historical volatility, market conditions, and statistical distributions.

You look at the distribution of outcomes.

If the risk-adjusted expected value is positive across enough simulations, you take the trade.

If it is not, you pass.

Every time. No emotion. No gut feeling. Pure math.

In 1988 this required a team of PhDs and millions in infrastructure.

In 2026 Claude Code builds the entire system in a weekend.

The stack:

Claude = the algorithm's brain. Writes the simulation logic, interprets results, and makes the execution decision.

MiroFish = the simulation engine. Runs 10,000 cycles against historical data.

The combination gives you institutional-grade trade validation from a laptop.

The knowledge gap between you and a quant fund trader has never been smaller.

The tools are available.

The question is whether you build the system.

Bookmark this.

Follow @cyrilXBT for the exact Claude Code setup to build this bot this weekend.

[winkle. @w1nklerr]

Two Chinese developers revealed a trading setup that generates about $90K per month

Their system relies on 7 AI agents that handle the entire process from start to finish

The full setup costs only $50 per month to run

At its core, everything is built specifically for trading

Across crypto and forex markets, the system constantly scans for high probability opportunities

Using a database of more than 100 trading setups, it enters trades instantly whenever conditions are met

Execution speed is extremely fast, latency is minimal, and the win rate is around 77% with a risk to reward ratio of 2 or higher

Daily profit ranges between $5K and $7K, which is exceptionally strong

While most traders spend time searching for opportunities, this system was built to create them

It runs 24 7 and operates fully passively without manual involvement

727 thousand people are now watching that wallet
The 7 agents are still answering tickets somewhere for $50 a month
The wallet behind them does not need 7 agents it needs one
And that one was already running while they were filming

[AdiiX @adiix_official]

Chinese trader on Polymarket, starting with a minimal capital of $84, quietly grew his account to $7.8M

He is the cleanest and most disciplined sports trader I’ve seen on the entire platform.

Profile: https://t.co/CoLr1VBPrm

Copytrading in one click: https://t.co/FOVhhdVdXl

His trading logic is exceptionally clear:

> He focuses exclusively on two narrow areas esports and traditional sports, with a particular emphasis on the NBA and League of Legends.

> He strictly avoids markets related to politics, weather, or cryptocurrency.
He executes between 150 and 550 trades per day, all manually, without using automated trading bots.
In total, he has completed 59,000 predictions with a success rate of up to 90%.

> He gradually scales into positions and consistently exits the market in advance, before excessive hype sets in.

The key point many people fail to understand:
His main advantage is not being faster than automated systems.

It’s that he only operates in markets he deeply understands and has studied over a long period of time.

In contrast to others who spread themselves across dozens of categories and incur constant losses, this trader always stays within his area of expertise and steadily generates profit.

[st1ne @SolSt1ne]

Chinese quant built a cross-chain arbitrage AI nobody thought was possible

$4,000 → $311,000 in one position.

7,775% return. Fully verifiable on Polymarket.

His wallet: https://t.co/K88MwJqWtU

Claude reads 47 simultaneous data streams. MiroFish stress-tests every single one across 20,000 parallel simulations.

The edge: he's connected to dark pool feeds from three institutional desks in Singapore and Hong Kong.

When arbitrage windows open -they last 0.3 seconds. His bot fires in 0.07.

April 27: entered BTC arb at 0.9% implied probability → $311k profit (77x).

Numbers like this don't come from luck. They come from math the market hasn't seen yet.

Want the full breakdown? Save this post.

Start copying now: https://t.co/CgPKduYWda

[Noisy @noisyb0y1]

A 16-year-old American student built an AI Hermes agent for Polymarket that trades the Bitcoin market - up or down in 5 minutes.

Most people walk in like it's a casino and leave with nothing.

He built a system that analyzes every candle before entry and gives a signal only when conditions line up - no emotions, no guessing, just data.

Last week - 76.7% win rate including bad market conditions where most traders don't open positions at all

> 18 winning trades in a row on real payouts ($100 -> $5000

The agent runs 24/7 while he sleeps - monitors the market, waits for the right signal and enters only when everything aligns.

[Daniro @Dan1ro0]

HOW DOES THIS QUANT BOT PRINT $3,617 IN PROFIT PER DAY

+$122,965 PnL in 34 days on Polymarket
Only on short crypto “Up / Down” markets

On Mar 26, this wallet switched to a high-frequency market-making style using Bayesian + Stoikov pricing rule

P(H|D) = P(D|H) · P(H) / P(D)

The top trades already show how this setup monetizes tiny dislocations:

$517.83 → $1,044.58 (+101.72%)
$507.75 → $1,012.57 (+99.42%)
$500.00 → $1,000.00 (+100%)

This Quant Bot’s wallet:https://t.co/kIaCST5Rjo

The account is already up to +$122,964 PnL after starting from a relatively small base.

f = (bp - q) / b* ;
Kelly is the sizing engine that decides how much capital should go into each edge

r = s - qγσ²(T - t) ;
Stoikov is the inventory-control layer that tells the bot where to quote and how to avoid getting too lopsided

So this is not some basic script spamming both sides and hoping one settles green

What is running here is a quant process that identifies bad pricing, scales exposure to the edge, and manages risk while the market is still moving.

Everything still revolves around one simple condition:

[ YES + NO < 100¢ ]

[AdiiX @adiix_official]

CHINESE AI DEV CONNECTED CLAUDE TO POLYMARKET AND TURNED $177 INTO $15,800 WITH ONE BET ON 5-MINUTE MARKETS

Past week: +$95,000.

+$13,500/day!

> 54 trades made >+$3,000
> max loss: -$2,986

Total PnL: +$115,000.

Profile : https://t.co/DAv2buAiPk

Copy trade him: https://t.co/FOVhhdVLMT

Best trades:
> $177 -> $15,809
> $409 -> $13,136
> $114 -> $11,437

5.35% of trades generated over $3,000 in profit!

Max loss is always smaller than any of these big wins.

He uses the Kelly Criterion to size positions optimally.

50% win rate, but profits are ~3x larger than losses -> growth.

The key is positive Expected Value (EV).

1009 trades and +$115,000 PnL - the stats speak for themselves.

With a $1,000 deposit, each copied trade averages +$11.4.

The more trades you copy, the closer your result gets to the average (law of large numbers):
> 50 trades = ~91% chance to be profitable
> 100 trades = ~96%
> 300+ trades = ~99%

Pure math.

Losses are capped, while gains consistently outweigh them.

Even with a 50% win rate, you can have positive EV.

I described this in detail in the article.

Save it

If you need a full step by step guide on how to write your own bot, just:

1.follow me
2.repost this post
3. comment “GUIDE”

And I will send you the full strategy for writing your own bot

[FHILY👑 @Oluwaphilemon1]

Breaking: Anthropic's CEO: "software engineering will be fully automated in 12 months."

two types of people right now:

type 1: uses Claude Code with basic prompts. scared about the future.

type 2: knows all 35 commands, tricks, and workflows. ships faster than a team of 5.

type 1 gets replaced in 12 months.
type 2 does the replacing.

There is a full guide on how to start. zero experience needed.

[Discover @0x_Discover]

Most traders try to predict the market.
He waits for it to break.

A Chinese quant turned $3,000 into $198,000
in a single trade.

Not luck. Not timing.
Just math.

+6,600% — fully onchain.

Every position visible:
https://t.co/PeeCtsKjPr

Copytrade https://t.co/IPY41UFgnZ

His edge?

He doesn’t guess outcomes.
He hunts mispriced probabilities.

When the odds stop making sense —
he goes in, hard.

25,000 probability recalculations
before every entry.

No narratives.
No emotions.
Just execution.

April 27:
Market priced it near impossible (~1.7%).

He saw the flaw.
Took the other side.
Extracted the liquidity.

$410K+ total profit.

He’s not predicting the future.
He’s exploiting when everyone else is wrong.

[cvxv666 @antpalkin]

Chinese quant built a perfect BTC price simulation engine with MiroFish

In a single trade, he turned $2,000 into $166,000.

7,500% profit. All proof onchain - every single one of his trades is publicly visible on Polymarket.

His wallet: https://t.co/G1EyL2Vjnq

His algorithm instantly detects any mispricing in crypto markets and enters trade immediately.

$350k all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else.

He’s using closed order book data + private OTC desks. Already elite alpha.

Then the real magic happens: 10,000 simulation cycles of how the market will react.

On April 24, he was only one who knew the market was wrong.

He entered a trade with just a 1.3% implied probability of execution.

This isn’t "guessing where the chart will go"

This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data.

Want to learn how to build something like this? Save the post and read the article.

If you don’t want to miss his next 75x trade, starting copy every one of his trades right now using this TG bot: https://t.co/vbDZyVcfT3

[0xMarioNawfal @RoundtableSpace]

A CHINESE STUDENT ALLEGEDLY TURNED $2K INTO $166K USING AI POWERED POLYMARKET TRADES.

The setup combined Claude with simulation engines and massive market datasets to identify ultra low probability opportunities before the market reacted.

[Discover @0x_Discover]

A 19-year-old student from Japan built an automated trading bot using Claude Code.

Claude handled most of the work.

The bot tracks price discrepancies across 50+

Polymarket markets while syncing BTC data from Binance via OpenClaw.

That’s where the edge comes from.

He built it in 2 days.
Used an iPad as a second screen.
Architecture:

Claude → strategy + mispricing detection
OpenClaw → execution + Binance sync
0 manual input

Performance:

One night → 3 trades opened, 2 closed

Example: BTC short (15m)
0.31 → 0.79
+$6,732

Logic:

Captures pricing inefficiencies between markets
High-frequency, low edge per trade
Profit comes from volume

Reference wallet:
https://t.co/XAGDeUr50O

Copytrade:https://t.co/IPY41UFgnZ

Stats:

54,356 trades
$1,720,270 profit
Pure arbitrage

Evolution:

Started with a few hundred
First copied trades
Then built his own system

Risk management:

Auto shutdown on low liquidity
Manual confirmation for emergency exits
Worst drawdown ~3%
Current state:

Fully autonomous
Only monitors notifications

Starting capital: ~$68

[Himanshu Kumar @codewithimanshu]

Jane Street hired a junior for $220K-$600K/year because he uses AI to analyze trillions of data points.

In this 1-hour lecture, he shows exactly how he does it.

Free. From the guy Wall Street is paying half a million to.

You've been using AI to write captions. He's using it to print money on trillion-row datasets.

Bookmark this instead of Netflix tonight. It pays for the rest of your career.

Follow @codewithimanshu for more high-signal AI content from the people actually building the future.
↓

What he actually does for that paycheck.

He builds machine learning systems for a trillion trillion floating point operations. Not "uses AI tools." Builds them. From scratch. At scale most engineers never touch in a full career.

He's on the PyTorch core team. The same PyTorch that powers Jane Street, OpenAI, Anthropic, and every serious AI shop on earth.

That's why the salary is $220K-$600K and not flat. Subpar year: $220K. Outstanding results: $600K+. Performance-based. Real impact. Real numbers.

Wall Street isn't paying for credentials anymore. They're paying for engineers who can move trillion-row datasets through ML systems faster than anyone else on the planet.

Follow @codewithimanshu for more breakdowns of the AI roles paying $500K+ in 2026.

↓

What this lecture actually teaches.

This is not "AI for beginners."

This is the exact technical foundation that turns a junior into the top-end of Jane Street's pay band:

> How to architect ML pipelines for trillion-scale datasets
> Why PyTorch internals matter at production scale
> The optimization tricks that turn 10-hour jobs into 10-minute ones
> Memory layouts and GPU kernels that hedge funds quietly weaponize
> The mental models behind systems that move billions in trades

This is Jane Street's edge being explained in public.

Most engineers will watch 5 minutes, get scared, and click away. The ones who push through become the next $500K hires.

Follow @codewithimanshu for breakdowns of every must-watch AI lecture worth your weekend.

↓

Why this matters more than any bootcamp.

A 12-week ML bootcamp: $10,000-$15,000.
A masters in ML at Stanford or CMU: $80,000+.
This 1-hour lecture from a Jane Street insider: free.

You've spent more on Uber Eats this month than this lecture costs.

The gap between engineers earning $120K and engineers earning $500K+ isn't talent. It's exposure to content like this.

People who watch it tonight understand AI infrastructure at the level Wall Street pays for.
People who skip it stay competing with millions of other "AI engineers" using the same ChatGPT prompts.

Same field. Different bank account.

Save the video. Watch it tonight. Become the kind of engineer Jane Street fights other firms to hire.

Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

[BillionAireSon 🛡️ @_BillionAireSon]

everyone in crypto has done this before. gm

[Wealthy Bitcoin 🛸 @wealthy_btc]

If you believe in the future of blockchain &amp; crypto 🚀
👉 Follow me for insights, analysis &amp; early opportunities!
Together, we make it 💪

[Wealthy Bitcoin 🛸 @wealthy_btc]

GM ☀️ I’m Saul, passionate about crypto &amp; blockchain.
With a background in traditional finance and 3+ years of crypto research &amp; analysis, I’ve seen both worlds.
Here’s what I’ve learned so far 👇

[Wealthy Bitcoin 🛸 @wealthy_btc]

My goal is to build a community of crypto investors &amp; learners.
No shilling, no pump &amp; dump.
Only real value.