Data Orchestrator

Data Orchestrator - AI Trading Data Strategy Layer

Central nervous system for all data operations across the meme-times ecosystem. Implements a robust, AI-ready data strategy that precedes and enables all trading decisions.

Core Principle

Data strategy comes BEFORE AI. Clean governance, validated sources, and secure infrastructure enable useful trading insights.

Activation Triggers

Data Strategy Pillars

1. Diverse Data Sources

On-Chain Data:

Sentiment & Social:

DeFi Protocol Data:

2. Data Quality & Governance

interface QualityThresholds { trading_signals: { min: 90, critical: 'timeliness' }; historical_analysis: { min: 85, critical: 'completeness' }; sentiment_analysis: { min: 70, critical: 'consistency' }; backtesting: { min: 95, critical: 'accuracy' }; }

**Validation Pipeline:**

Raw Data → Schema Validation → Anomaly Detection → Cross-Source Check → Quality Score → Accept/Reject

**Validation Rules:**
1. **Schema Validation**: All data must match expected types/formats
2. **Range Checks**: Prices, volumes, percentages within valid bounds
3. **Anomaly Detection**: Flag outliers > 3 standard deviations
4. **Cross-Source Verification**: Compare with 2+ sources for critical data
5. **Freshness Enforcement**: Reject stale data beyond threshold

**Automated Quality Monitoring:**
```bash
# Run continuous quality checks
npx tsx .claude/skills/data-orchestrator/scripts/quality-monitor.ts \
  --sources "dexscreener,birdeye,jupiter" \
  --interval 60 \
  --alert-threshold 80

3. Real-Time Data Pipeline Architecture

Data Flow Configuration:

interface PipelineConfig {
  sources: DataSource[];
  validationRules: ValidationRule[];
  enrichmentSteps: EnrichmentFunction[];
  storageTargets: StorageTarget[];
  alertsEnabled: boolean;
  qualityThreshold: number;
}

const defaultPipeline: PipelineConfig = {
  sources: [
    { name: 'dexscreener', type: 'websocket', priority: 1 },
    { name: 'birdeye', type: 'rest', priority: 2 },
    { name: 'jupiter', type: 'rest', priority: 3 },
  ],
  validationRules: ['schema', 'range', 'anomaly', 'freshness'],
  enrichmentSteps: ['normalize', 'calculate_indicators', 'tag_quality'],
  storageTargets: ['redis:hot', 'sqlite:warm'],
  alertsEnabled: true,
  qualityThreshold: 85,
};

4. ML/AI Integration Framework

interface TimeSeriesFeatures { values: number[]; timestamps: Date[]; normalized: number[]; // Z-score normalized lagged: number[][]; // [lag_1, lag_5, lag_15, lag_60] rolling_stats: { mean_5: number[]; std_5: number[]; mean_15: number[]; std_15: number[]; }; }

**Supported ML Techniques:**
1. **Anomaly Detection**: Isolation forests for unusual price/volume patterns
2. **NLP/Sentiment**: BERT-based sentiment from social feeds
3. **Time Series**: LSTM/Transformer for price prediction
4. **Classification**: XGBoost for buy/sell signal classification
5. **Reinforcement Learning**: DQN for optimal trade execution

**Continuous Learning Pipeline:**

New Data → Feature Extraction → Model Inference → Signal Generation ↑ Performance Feedback ↓ Model Retraining (Weekly)

</ml_integration>

### 5. Backtesting Infrastructure

<backtesting>
**Historical Data Requirements:**
```typescript
interface BacktestDataset {
  token: string;
  timeframe: '1m' | '5m' | '15m' | '1h' | '4h' | '1d';
  start_date: Date;
  end_date: Date;
  data_points: {
    timestamp: Date;
    open: number;
    high: number;
    low: number;
    close: number;
    volume: number;
    liquidity: number;
    holders: number;
    sentiment_score?: number;
  }[];
  quality_metrics: DataQualityMetrics;
}

Backtest Execution:

# Run backtest with historical data
npx tsx .claude/skills/data-orchestrator/scripts/backtest-runner.ts \
  --strategy "momentum" \
  --token "BONK" \
  --start "2024-01-01" \
  --end "2024-12-01" \
  --initial-capital 1000 \
  --slippage 0.01

Backtest Report Output:

BACKTEST REPORT: Momentum Strategy on BONK
Period: 2024-01-01 to 2024-12-01

PERFORMANCE:
- Total Return: +234.5%
- Sharpe Ratio: 1.87
- Max Drawdown: -28.3%
- Win Rate: 62.4%
- Profit Factor: 2.15

TRADES:
- Total Trades: 156
- Avg Trade Duration: 4.2 hours
- Best Trade: +45.2%
- Worst Trade: -12.8%

DATA QUALITY:
- Coverage: 99.2%
- Missing Points: 847 / 105,120
- Quality Score: 94/100

CAVEATS:
- Historical results do not guarantee future performance
- Slippage model: 1% (actual may vary)
- Does not account for: MEV, extreme volatility periods

6. Risk & Portfolio Data Layer

Risk Data Sources:

• Position tracking from meme-executor
• Price volatility from historical data
• Correlation matrix from cross-asset analysis
• Liquidity depth from DEX APIs

Implementation Scripts

Data Pipeline Manager

# Start the data pipeline
npx tsx .claude/skills/data-orchestrator/scripts/pipeline-manager.ts \
  --mode production \
  --sources all \
  --storage sqlite,redis

# Validate specific data source
npx tsx .claude/skills/data-orchestrator/scripts/validate-source.ts \
  --source dexscreener \
  --token "BONK" \
  --verbose

# Generate ML-ready dataset
npx tsx .claude/skills/data-orchestrator/scripts/ml-dataset-builder.ts \
  --token "BONK" \
  --features "price,volume,sentiment" \
  --lookback 30 \
  --output ./datasets/bonk_ml_ready.parquet

Integration with Other Skills

Data Orchestrator receives from:

• All skills: Data quality feedback, missing data requests
• meme-executor: Trade execution data for backtest validation

Quality Gates

Error Handling

Compliance & Security

Performance Targets