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

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:

1. Anomaly Detection: Flag unusual volume/price patterns

   • Isolation forest on 24h price/volume deviation
   • Alert when score > 0.8 (potential pump or dump)

2. Sentiment Classification: Social momentum scoring

   • NLP analysis of Twitter/Telegram mentions
   • Bullish/Bearish/Neutral with confidence score

3. Pattern Recognition: Historical pattern matching

   • Compare current setup to 1000+ historical pumps
   • Match score indicates similarity to successful entries

4. Predictive Indicators: ML-derived signals

   • 1h price direction probability (up/down/sideways)
   • Optimal entry window prediction
   • Volume momentum forecast

Signal Confidence Framework:

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

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:

npx tsx .claude/skills/meme-trader/scripts/fetch-meme-data.ts \
  --token "PUMP123...abc" \
  --action analyze \
  --risk degen

Step 3: Analysis Pipeline

1. Contract Check � Verify no malicious functions
2. Liquidity Check � Assess depth and lock status
3. Holder Analysis � Distribution and whale activity
4. Social Scan � Sentiment and narrative strength
5. 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):

• Mint authority NOT renounced
• Freeze authority enabled
• Hidden transfer fees > 5%
• Liquidity < $10K
• LP not locked
• Top holder > 20% (non-exchange)

WARNING (Proceed with caution):

• Dev wallet holds > 5%
• < 100 holders
• No social presence
• Copied contract (no modifications)
• Launch < 1 hour ago

GREEN FLAGS:

• Mint renounced + freeze disabled
• LP locked 3+ months
• Top 10 holders < 30%
• Active community (TG/Twitter)
• KOL/influencer backing
• Audited contract </rug_indicators>

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

<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>