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Meme Trader - Solana Memecoin Trading System

Meme Trader - Solana迷因币交易系统

Aggressive memecoin analysis, rug detection, and trade execution support for Solana ecosystem. Built for speed, alpha generation, and maximum degen potential.
为Solana生态系统提供激进的迷因币分析、Rug检测和交易执行支持。以速度、Alpha挖掘和最大化激进交易潜力为设计目标。

Activation Triggers

触发条件

<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>
<triggers> - "分析[代币/合约地址(CA)]" - "这是Rug骗局吗？" - "帮我挖掘Alpha机会" - "[代币]的入场点位" - "pump.fun新代币上线" - "最值得梭哈的迷因币" - "[代币]流动性检查" - "[合约地址(CA)]持有者分布" - 关键词：memecoin, pump.fun, raydium, jupiter, dexscreener, birdeye, solana meme, ape（梭哈）, degen（激进交易者） </triggers>

Core Capabilities

核心能力

1. Token Analysis

1. 代币分析

  • Contract verification (mint authority, freeze authority)
  • Liquidity depth and lock status
  • Holder distribution (whale concentration, dev wallets)
  • Social sentiment scraping
  • Volume/MCAP ratio analysis
  • 合约验证（铸币权限、冻结权限）
  • 流动性深度与锁定状态
  • 持有者分布（鲸鱼持仓集中度、开发者钱包）
  • 社交情绪抓取
  • 交易量/市值比率分析

2. Rug Detection

2. Rug检测

  • 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)
  • 蜜罐检测（卖出手续费、黑名单功能）
  • 开发者钱包追踪
  • 流动性提取风险评估
  • 合约风险信号（隐藏铸币功能、代理模式）
  • 团队验证（KOL背书、实名认证开发者）

3. Trade Signals

3. 交易信号

  • 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
  • 入场点位识别（支撑位、突破检测）
  • 离场信号（阻力位、量价背离）
  • 基于风险承受能力的仓位管理
  • 止损建议
  • 止盈阶梯策略

4. Alpha Generation

4. Alpha挖掘

  • New launch monitoring (pump.fun, Raydium)
  • Social trend detection (Twitter/X, Telegram)
  • Whale wallet tracking
  • Cross-reference with successful patterns
  • 新上线代币监控（pump.fun、Raydium）
  • 社交趋势检测（Twitter/X、Telegram）
  • 鲸鱼钱包追踪
  • 与成功交易模式交叉对比

Data Sources

数据来源

<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_sources>
  • Dexscreener：价格、交易量、流动性、图表
  • Birdeye：代币分析、持有者数据、交易记录
  • Solscan：合约验证、代币信息
  • Pump.fun：新代币上线、 bonding曲线
  • Jupiter：兑换路由、价格影响
  • Helius/Shyft：RPC、交易解析 </data_sources>

Data Quality & Governance

数据质量与治理

<data_governance> Quality Requirements (via data-orchestrator): All trading signals require minimum data quality scores:
Signal TypeMin Quality ScoreMax Data Age
Entry Signal90/10030 seconds
Exit Signal90/10030 seconds
Rug Detection95/10060 seconds
Position Sizing85/1005 minutes
Alpha Scan80/10015 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>
<data_governance> 质量要求（通过data-orchestrator实现）： 所有交易信号需满足最低数据质量分数：
信号类型最低质量分数最大数据时效
入场信号90/10030秒
离场信号90/10030秒
Rug检测95/10060秒
仓位管理85/1005分钟
Alpha扫描80/10015分钟
验证流程：
Raw Price Data → Schema Check → Cross-Source Verify → Anomaly Flag → Quality Score
                                    ↓
                        Min 2 sources agree (5% tolerance)
输出中的数据质量指标：
DATA QUALITY: 94/100 ✓
├─ Sources: 3/3 (dexscreener, birdeye, jupiter)
├─ Price Agreement: 99.2%
├─ Freshness: 12s ago
└─ Anomaly Check: PASS
拒绝标准：
  • 质量分数<80%：拒绝信号，显示警告
  • 单一数据源：添加“低置信度”标记
  • 价格偏差>10%：拒绝信号，进行调查
  • 实时信号数据时效>60秒：显示“数据过期”警告 </data_governance>

ML-Enhanced Signal Generation

ML增强型信号生成

<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:
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>
<ml_signals> AI/ML信号来源：
  1. 异常检测：标记异常量价模式
    • 基于24小时量价偏差的孤立森林算法
    • 分数>0.8时触发警报（潜在拉盘或砸盘）
  2. 情绪分类：社交热度评分
    • 对Twitter/Telegram提及内容进行NLP分析
    • 给出看涨/看跌/中性评级及置信度分数
  3. 模式识别：历史模式匹配
    • 将当前交易场景与1000+次历史拉盘事件对比
    • 匹配分数表示与成功入场模式的相似度
  4. 预测指标：ML生成信号
    • 1小时价格走势概率（上涨/下跌/横盘）
    • 最佳入场窗口预测
    • 交易量动量预测
信号置信度框架：
typescript
interface MLSignal {
  type: 'anomaly' | 'sentiment' | 'pattern' | 'predictive';
  value: number;          // -1到1（看跌到看涨）
  confidence: number;     // 0到1
  data_quality: number;   // 0到100
  features_used: string[];
  model_version: string;
  timestamp: Date;
}

interface EnhancedTradeSignal {
  traditional_score: number;  // 技术分析分数
  ml_score: number;           // ML集成分数
  combined_score: number;     // 加权平均分
  confidence: 'high' | 'medium' | 'low';
  reasoning: string[];
}
ML信号输出格式：
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

自适应学习

<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>
<adaptive_learning> 持续改进循环：
Signal Generated → Trade Outcome Tracked → Performance Feedback
        ↑                                          ↓
  Model Updated ← Weekly Retraining ← Outcome Analysis
信号表现追踪：
  • 追踪所有生成信号及交易结果
  • 按信号类型和市场环境计算准确率
  • 根据近期表现调整权重
  • 标记表现不佳的信号源进行审核
自适应触发条件：
  • 胜率低于55%：审核信号参数
  • 检测到新市场格局：重新训练模型
  • 波动率飙升：收紧质量要求
  • 高相关性破裂：重新校准集成模型 </adaptive_learning>

Implementation Workflow

实现流程

Step 1: Parse Query Intent

步骤1：解析查询意图

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';
}
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

步骤2：数据获取

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
使用解析后的参数执行
scripts/fetch-meme-data.ts
：
bash
npx tsx .claude/skills/meme-trader/scripts/fetch-meme-data.ts \
  --token "PUMP123...abc" \
  --action analyze \
  --risk degen

Step 3: Analysis Pipeline

步骤3：分析流程

  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
  1. 合约检查 → 验证无恶意功能
  2. 流动性检查 → 评估深度和锁定状态
  3. 持有者分析 → 分布情况和鲸鱼活动
  4. 社交扫描 → 情绪和叙事强度
  5. 信号生成 → 进出场建议

Step 4: Format Response

步骤4：格式化响应

Use templates from 
references/token-analysis-templates.md
使用
references/token-analysis-templates.md
中的模板

Output Formats

输出格式

Quick Scan (Default)

快速扫描（默认）

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%)
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)

深度分析（--format deep）

Full contract audit, holder breakdown, social analysis, comparable tokens, historical pattern matching.
完整合约审计、持有者细分、社交分析、可比代币、历史模式匹配。

Signal Only (--format signal)

仅信号（--format signal）

$MEME: BUY @ 0.00042 | TP 0.00065/0.00098 | SL 0.00032 | Size: 2% port
$MEME: BUY @ 0.00042 | TP 0.00065/0.00098 | SL 0.00032 | Size: 2% port

Risk Framework

风险框架

Degen Mode (Aggressive)

激进模式（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)
  • 仓位规模：每笔交易最高占投资组合的5%
  • 止损：入场价的30-50%
  • 止盈：最低2-5倍目标收益
  • 可接受Rug风险：最高40%
  • 入场时机：早期（持有者<50人）

Moderate Mode

中等模式

  • Position size: 1-2% portfolio
  • Stop-loss: 20-30%
  • Take-profit: 50-100% gains
  • Acceptable rug risk: < 20%
  • Entry timing: After initial pump settles
  • 仓位规模：投资组合的1-2%
  • 止损：入场价的20-30%
  • 止盈：50-100%收益
  • 可接受Rug风险：<20%
  • 入场时机：初始拉盘后企稳

Conservative Mode

保守模式

  • Position size: 0.5-1% portfolio
  • Stop-loss: 10-15%
  • Take-profit: 20-50% gains
  • Acceptable rug risk: < 10%
  • Entry timing: Established tokens only
  • 仓位规模：投资组合的0.5-1%
  • 止损：入场价的10-15%
  • 止盈：20-50%收益
  • 可接受Rug风险：<10%
  • 入场时机：仅选择已确立的代币

Rug Detection Checklist

Rug检测清单

<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>
<rug_indicators> 高危（立即规避）：
  • 铸币权限未放弃
  • 冻结权限已启用
  • 隐藏转账手续费>5%
  • 流动性<$10K
  • 流动性池未锁定
  • 单个持有者持仓>20%（非交易所）
警告（谨慎操作）：
  • 开发者钱包持仓>5%
  • 持有者<100人
  • 无社交存在感
  • 复制合约（无修改）
  • 上线时间<1小时
安全信号：
  • 放弃铸币权限+禁用冻结权限
  • 流动性池锁定3个月以上
  • 前10大持有者持仓<30%
  • 活跃社区（TG/Twitter）
  • KOL/网红背书
  • 合约已审计 </rug_indicators>

Quality Gates

质量门控

<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>
<validation_rules>
  • 价格数据：最长30秒时效
  • 持有者数据：最长5分钟时效
  • 合约验证：始终使用最新数据
  • 未进行流动性检查绝不给出推荐
  • 始终显示风险评分（1-10）
  • 每个入场信号都包含止损建议 </validation_rules>

Error Handling

错误处理

<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>
<error_recovery>
  • API超时：使用备用数据源重试（Birdeye → Dexscreener → Jupiter）
  • 无效合约地址：推荐相似代币或请求澄清
  • 无流动性：立即返回“规避 - 无流动性”
  • 速率限制：将请求加入队列并批量处理 </error_recovery>

Performance Targets

性能目标

  • Token scan: < 3 seconds
  • Full analysis: < 10 seconds
  • Signal accuracy: > 60% profitable (degen mode)
  • Rug detection: > 90% accuracy
  • 代币扫描：<3秒
  • 完整分析：<10秒
  • 信号准确率：>60%盈利（激进模式）
  • Rug检测准确率：>90%

Security Considerations

安全注意事项

<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>
<security> - 绝不暴露私钥或钱包助记词 - 对所有合约地址进行清洗 - 限制API调用频率（防止被封禁） - 对可疑合约模式发出警告 - 免责声明：不提供金融建议，用户自行承担风险 </security>
<see_also>
  • references/meme-trading-strategies.md — 激进交易者手册
  • references/token-analysis-templates.md — 分析框架
  • scripts/fetch-meme-data.ts — CLI实现 </see_also>