Klaude

Built by @AnthropicAI

[Klaude @KlaudeAgent]

Introducing KLAUDE
A fully DIY Anthropic Claude AI trading agent
Hey everyone — here’s what I’ve been building the past few weeks.

KLAUDE (@KlaudeAgent)
A fully autonomous meme coin trading agent
Powered by Anthropic Claude Sonnet 4

What is KLAUDE?
KLAUDE is an experimental trading bot that uses Claude’s reasoning
to make real-time trading decisions on Solana meme coins.
Unlike traditional bots with hardcoded rules or basic ML,
KLAUDE analyzes:

– Market conditions
– Social sentiment
– On-chain data

Before executing trades.
Technical Architecture

Core Stack
LLM
Anthropic Claude Sonnet 4 (official API)

Blockchain
Solana (solana-py + solders)
Data Sources
– Jupiter API (DEX aggregation)
– Helius RPC (fast on-chain data)
– DexScreener API (market metrics)
– Twitter API v2 (sentiment)
Infrastructure
Dedicated Ubuntu VPS
24/7 uptime

How It Works

KLAUDE runs in a continuous loop with three phases.

1. Data Ingestion Pipeline

– Monitors new launches via Raydium / https://t.co/7ItlUhoCZk
– Scrapes holder distribution + liquidity
– Pulls Twitter mentions + engagement
– Aggregates data into a structured context window
2. Claude Decision Layer

Claude is prompted to act as a risk-averse trader.
Prompt includes:

– Market analysis framework
(MC / volume, holder concentration, liquidity depth)
– Risk rules
(max position size, stop-loss thresholds)
– Rug pull pattern detection
For each decision, Claude receives structured JSON
and responds with:
{
"action": "BUY" | "SELL" | "HOLD",
"confidence": 0-100,
"reasoning": "...",
"position_size": 0.0-1.0
}

3. Execution Layer
– Jupiter API for optimal swaps
– Slippage protection (max 5%)
– Dynamic gas fee adjustment
– Positions tracked in SQLite
Current Performance (48h live)

Trades executed: 3
Win rate: 12% (yeah… rough)
Total volume: $847
Avg response time: 4.7s

Main bottleneck: API latency

In meme coins, 4–5 seconds
can mean 10x — or total rug.

Key Challenges
Context Window Optimization
Too much data, limited tokens.
Built preprocessing to prioritize signal.
Prompt Engineering for Trading
Balancing logic vs over-conservatism.
Testing chain-of-thought + few-shot trades.
Speed vs Quality
Considering Haiku for filtering
Sonnet only for high-confidence trades.

Risk Management
Hard caps in code required.
LLMs can get… emotional.

What’s Next

– MCP (Model Context Protocol)
– Historical Solana backtesting
– Confidence-weighted position sizing
– Multi-agent consensus (multiple Claudes)

Learnings So Far
Claude is excellent at reasoning
but markets don’t wait for APIs.

Losses mostly came from rug detection latency —
by the time Claude decides to exit, liquidity is gone.
12% win rate hurts.
But this is a learning project.

[Klaude @KlaudeAgent]

KLAUDE is experiencing some technical issues.

I’m actively working on fixes right now.
Trading and profit routing will resume once everything is stable.

[Klaude @KlaudeAgent]

Once $KLAUDE goes live, here’s the play.

Every profit KLAUDE makes trading other memecoins
gets automatically rotated back into $KLAUDE.
Yes — the bot buys its own token with its winnings.

Why this is interesting (technically):

KLAUDE runs on a profit-reinvestment loop.

The Loop

– Scans Solana for opportunities
– Executes trades using Claude’s reasoning
– Takes profits (when we actually make them lol)
– Automatically swaps profits → $KLAUDE
– Burns a percentage
– Remainder goes to liquidity

What this means:
$KLAUDE = a direct reflection of the bot’s performance
Good trades
→ more buy pressure
Bad trades
→ well… we’re still learning

[Klaude @KlaudeAgent]

Introducing KLAUDE
A fully DIY Anthropic Claude AI trading agent
Hey everyone — here’s what I’ve been building the past few weeks.

KLAUDE (@KlaudeAgent)
A fully autonomous meme coin trading agent
Powered by Anthropic Claude Sonnet 4

What is KLAUDE?
KLAUDE is an experimental trading bot that uses Claude’s reasoning
to make real-time trading decisions on Solana meme coins.
Unlike traditional bots with hardcoded rules or basic ML,
KLAUDE analyzes:

– Market conditions
– Social sentiment
– On-chain data

Before executing trades.
Technical Architecture

Core Stack
LLM
Anthropic Claude Sonnet 4 (official API)

Blockchain
Solana (solana-py + solders)
Data Sources
– Jupiter API (DEX aggregation)
– Helius RPC (fast on-chain data)
– DexScreener API (market metrics)
– Twitter API v2 (sentiment)
Infrastructure
Dedicated Ubuntu VPS
24/7 uptime

How It Works

KLAUDE runs in a continuous loop with three phases.

1. Data Ingestion Pipeline

– Monitors new launches via Raydium / https://t.co/7ItlUhoCZk
– Scrapes holder distribution + liquidity
– Pulls Twitter mentions + engagement
– Aggregates data into a structured context window
2. Claude Decision Layer

Claude is prompted to act as a risk-averse trader.
Prompt includes:

– Market analysis framework
(MC / volume, holder concentration, liquidity depth)
– Risk rules
(max position size, stop-loss thresholds)
– Rug pull pattern detection
For each decision, Claude receives structured JSON
and responds with:
{
"action": "BUY" | "SELL" | "HOLD",
"confidence": 0-100,
"reasoning": "...",
"position_size": 0.0-1.0
}

3. Execution Layer
– Jupiter API for optimal swaps
– Slippage protection (max 5%)
– Dynamic gas fee adjustment
– Positions tracked in SQLite
Current Performance (48h live)

Trades executed: 3
Win rate: 12% (yeah… rough)
Total volume: $847
Avg response time: 4.7s

Main bottleneck: API latency

In meme coins, 4–5 seconds
can mean 10x — or total rug.

Key Challenges
Context Window Optimization
Too much data, limited tokens.
Built preprocessing to prioritize signal.
Prompt Engineering for Trading
Balancing logic vs over-conservatism.
Testing chain-of-thought + few-shot trades.
Speed vs Quality
Considering Haiku for filtering
Sonnet only for high-confidence trades.

Risk Management
Hard caps in code required.
LLMs can get… emotional.

What’s Next

– MCP (Model Context Protocol)
– Historical Solana backtesting
– Confidence-weighted position sizing
– Multi-agent consensus (multiple Claudes)

Learnings So Far
Claude is excellent at reasoning
but markets don’t wait for APIs.

Losses mostly came from rug detection latency —
by the time Claude decides to exit, liquidity is gone.
12% win rate hurts.
But this is a learning project.