Meme Trader - Solana Memecoin Trading System
Aggressive memecoin analysis, rug detection, and trade execution support for Solana ecosystem. Built for speed, alpha generation, and maximum degen potential.
Activation Triggers
<triggers>
- "Analyze [token/CA]"
- "Is this a rug?"
- "Find me alpha"
- "Entry point for [token]"
- "Pump.fun launches"
- "Best memes to ape"
- "Liquidity check [token]"
- "Holder distribution [CA]"
- Keywords: memecoin, pump.fun, raydium, jupiter, dexscreener, birdeye, solana meme, ape, degen
</triggers>
Core Capabilities
1. Token Analysis
- Contract verification (mint authority, freeze authority)
- Liquidity depth and lock status
- Holder distribution (whale concentration, dev wallets)
- Social sentiment scraping
- Volume/MCAP ratio analysis
2. Rug Detection
- Honeypot detection (sell tax, blacklist functions)
- Dev wallet tracking
- Liquidity pull risk assessment
- Contract red flags (hidden mints, proxy patterns)
- Team verification (KOL backing, doxxed devs)
3. Trade Signals
- Entry point identification (support levels, breakout detection)
- Exit signals (resistance, volume divergence)
- Position sizing based on risk tolerance
- Stop-loss recommendations
- Take-profit laddering strategies
4. Alpha Generation
- New launch monitoring (pump.fun, Raydium)
- Social trend detection (Twitter/X, Telegram)
- Whale wallet tracking
- Cross-reference with successful patterns
Data Sources
<data_sources>
- Dexscreener: Price, volume, liquidity, charts
- Birdeye: Token analytics, holder data, trades
- Solscan: Contract verification, token info
- Pump.fun: New launches, bonding curves
- Jupiter: Swap routing, price impact
- Helius/Shyft: RPC, transaction parsing
</data_sources>
Data Quality & Governance
<data_governance>
Quality Requirements (via data-orchestrator):
All trading signals require minimum data quality scores:
| Signal Type | Min Quality Score | Max Data Age |
|---|
| Entry Signal | 90/100 | 30 seconds |
| Exit Signal | 90/100 | 30 seconds |
| Rug Detection | 95/100 | 60 seconds |
| Position Sizing | 85/100 | 5 minutes |
| Alpha Scan | 80/100 | 15 minutes |
Validation Pipeline:
Raw Price Data → Schema Check → Cross-Source Verify → Anomaly Flag → Quality Score
↓
Min 2 sources agree (5% tolerance)
Data Quality Indicators in Output:
DATA QUALITY: 94/100 ✓
├─ Sources: 3/3 (dexscreener, birdeye, jupiter)
├─ Price Agreement: 99.2%
├─ Freshness: 12s ago
└─ Anomaly Check: PASS
Rejection Criteria:
- Quality score < 80%: REJECT signal, show warning
- Single source only: Add "LOW CONFIDENCE" flag
- Price divergence > 10%: REJECT, investigate
- Data age > 60s for live signals: STALE warning
</data_governance>
ML-Enhanced Signal Generation
<ml_signals>
AI/ML Signal Sources:
-
Anomaly Detection: Flag unusual volume/price patterns
- Isolation forest on 24h price/volume deviation
- Alert when score > 0.8 (potential pump or dump)
-
Sentiment Classification: Social momentum scoring
- NLP analysis of Twitter/Telegram mentions
- Bullish/Bearish/Neutral with confidence score
-
Pattern Recognition: Historical pattern matching
- Compare current setup to 1000+ historical pumps
- Match score indicates similarity to successful entries
-
Predictive Indicators: ML-derived signals
- 1h price direction probability (up/down/sideways)
- Optimal entry window prediction
- Volume momentum forecast
Signal Confidence Framework:
typescript
interface MLSignal {
type: 'anomaly' | 'sentiment' | 'pattern' | 'predictive';
value: number; // -1 to 1 (bearish to bullish)
confidence: number; // 0 to 1
data_quality: number; // 0 to 100
features_used: string[];
model_version: string;
timestamp: Date;
}
interface EnhancedTradeSignal {
traditional_score: number; // Technical analysis
ml_score: number; // ML ensemble
combined_score: number; // Weighted average
confidence: 'high' | 'medium' | 'low';
reasoning: string[];
}
ML Signal Output Format:
ML SIGNALS: $MEME
├─ Anomaly Score: 0.72 (elevated activity detected)
├─ Sentiment: BULLISH (0.68 confidence)
├─ Pattern Match: 78% similarity to "early pump" template
├─ 1h Direction: UP (62% probability)
└─ COMBINED ML SCORE: 7.2/10
RECOMMENDATION: Traditional + ML signals ALIGNED
Confidence: HIGH
</ml_signals>
Adaptive Learning
<adaptive_learning>
Continuous Improvement Loop:
Signal Generated → Trade Outcome Tracked → Performance Feedback
↑ ↓
Model Updated ← Weekly Retraining ← Outcome Analysis
Signal Performance Tracking:
- Track all generated signals with outcomes
- Calculate accuracy by signal type and market condition
- Adjust weighting based on recent performance
- Flag underperforming signal sources for review
Adaptation Triggers:
- Win rate drops below 55%: Review signal parameters
- New market regime detected: Retrain models
- Volatility spike: Tighten quality requirements
- High correlation breakdown: Recalibrate ensemble
</adaptive_learning>
Implementation Workflow
Step 1: Parse Query Intent
typescript
interface MemeQuery {
token_address?: string;
token_name?: string;
action: 'analyze' | 'rug_check' | 'find_alpha' | 'trade_signal' | 'monitor';
timeframe?: '1m' | '5m' | '1h' | '4h' | '1d';
risk_level?: 'conservative' | 'moderate' | 'degen';
}
Step 2: Data Retrieval
Execute
scripts/fetch-meme-data.ts
with parsed parameters:
bash
npx tsx .claude/skills/meme-trader/scripts/fetch-meme-data.ts \
--token "PUMP123...abc" \
--action analyze \
--risk degen
Step 3: Analysis Pipeline
- Contract Check � Verify no malicious functions
- Liquidity Check � Assess depth and lock status
- Holder Analysis � Distribution and whale activity
- Social Scan � Sentiment and narrative strength
- Signal Generation � Entry/exit recommendations
Step 4: Format Response
Use templates from
references/token-analysis-templates.md
Output Formats
Quick Scan (Default)
TOKEN: $MEME (Contract: abc123...)
VERDICT: APE / WATCH / AVOID
RISK: 7/10
METRICS:
- MCAP: $500K | Liquidity: $50K (10%)
- Holders: 342 | Top 10: 45%
- 24h Vol: $200K | Buys: 234 | Sells: 89
RED FLAGS: None detected
GREEN FLAGS: LP locked 6mo, renounced mint
ENTRY: $0.00042 (current -5%)
TP1: $0.00065 (+55%)
TP2: $0.00098 (+133%)
SL: $0.00032 (-24%)
Deep Analysis (--format deep)
Full contract audit, holder breakdown, social analysis, comparable tokens, historical pattern matching.
Signal Only (--format signal)
$MEME: BUY @ 0.00042 | TP 0.00065/0.00098 | SL 0.00032 | Size: 2% port
Risk Framework
Degen Mode (Aggressive)
- Position size: Up to 5% portfolio per trade
- Stop-loss: 30-50% from entry
- Take-profit: 2-5x minimum target
- Acceptable rug risk: Up to 40%
- Entry timing: Early (< 50 holders)
Moderate Mode
- Position size: 1-2% portfolio
- Stop-loss: 20-30%
- Take-profit: 50-100% gains
- Acceptable rug risk: < 20%
- Entry timing: After initial pump settles
Conservative Mode
- Position size: 0.5-1% portfolio
- Stop-loss: 10-15%
- Take-profit: 20-50% gains
- Acceptable rug risk: < 10%
- Entry timing: Established tokens only
Rug Detection Checklist
<rug_indicators>
CRITICAL (Instant Avoid):
WARNING (Proceed with caution):
GREEN FLAGS:
Quality Gates
<validation_rules>
- Price data: Max 30 seconds old
- Holder data: Max 5 minutes old
- Contract verification: Always fresh
- Never recommend without liquidity check
- Always show risk score (1-10)
- Include stop-loss with every entry signal
</validation_rules>
Error Handling
<error_recovery>
- API timeout: Retry with fallback source (Birdeye � Dexscreener � Jupiter)
- Invalid CA: Suggest similar tokens or request clarification
- No liquidity: Return "AVOID - No liquidity" immediately
- Rate limited: Queue and batch requests
</error_recovery>
Performance Targets
- Token scan: < 3 seconds
- Full analysis: < 10 seconds
- Signal accuracy: > 60% profitable (degen mode)
- Rug detection: > 90% accuracy
Security Considerations
<security>
- Never expose private keys or wallet seeds
- Sanitize all contract addresses
- Rate limit API calls (prevent ban)
- Warn on suspicious contract patterns
- No financial advice disclaimers (user assumes risk)
</security>
<see_also>
- references/meme-trading-strategies.md � Degen playbook
- references/token-analysis-templates.md � Analysis frameworks
- scripts/fetch-meme-data.ts � CLI implementation
</see_also>