Skip to content

Latest commit

 

History

History
231 lines (169 loc) · 6.39 KB

api_reference.md

File metadata and controls

231 lines (169 loc) · 6.39 KB

DeepChain API Reference

Core Modules

Strategy Module

The strategy module provides interfaces for implementing trading strategies.

BaseStrategy

Base class for all trading strategies.

class BaseStrategy:
    def train(self, data: Dict[str, np.ndarray]) -> None:
        """Train the strategy."""
        
    def predict(self, state: Dict[str, float]) -> int:
        """Predict action for given state."""
        
    def save(self, path: str) -> None:
        """Save strategy to disk."""
        
    def load(self, path: str) -> None:
        """Load strategy from disk."""

RLStrategy

Base class for reinforcement learning strategies.

class RLStrategy(BaseStrategy):
    def get_action_space(self) -> Dict[str, Any]:
        """Get action space definition."""
        
    def get_state_space(self) -> Dict[str, Any]:
        """Get state space definition."""
        
    def get_reward(self, state: Dict[str, float], action: int) -> float:
        """Calculate reward for state-action pair."""

Blockchain Module

The blockchain module provides interfaces for strategy validation.

BaseValidator

Base class for blockchain validators.

class BaseValidator:
    def validate_strategy(self, strategy_id: str) -> bool:
        """Validate a strategy."""
        
    def register_strategy(self, strategy_id: str, metadata: Dict[str, Any]) -> str:
        """Register a strategy on the blockchain."""
        
    def verify_execution(self, strategy_id: str, data: Dict[str, Any], proof: Any) -> bool:
        """Verify strategy execution."""

ZKValidator

Zero-knowledge proof based validator.

class ZKValidator(BaseValidator):
    def generate_proof(self, strategy_id: str, data: Dict[str, Any]) -> Any:
        """Generate execution proof."""
        
    def verify_proof(self, proof: Any) -> bool:
        """Verify zero-knowledge proof."""

Edge Module

The edge module provides interfaces for model deployment.

BaseDeployer

Base class for edge deployment.

class BaseDeployer:
    def compress_model(self, model: Any, target_size: int) -> Any:
        """Compress model to target size."""
        
    def export_model(self, model: Any, format: str, path: str) -> None:
        """Export model to specified format."""
        
    def validate_performance(self, model: Any, data: Dict[str, Any]) -> Dict[str, float]:
        """Validate model performance."""

EdgeOptimizer

Advanced edge deployment optimizer.

class EdgeOptimizer(BaseDeployer):
    def quantize_model(self, model: Any) -> Any:
        """Quantize model weights."""
        
    def prune_model(self, model: Any, ratio: float) -> Any:
        """Prune model weights."""

Monitoring Module

The monitoring module provides interfaces for system monitoring.

MetricsCollector

Collects and aggregates system metrics.

class MetricsCollector:
    def record_latency(self, latency: float) -> None:
        """Record prediction latency."""
        
    def record_prediction(self, prediction: Any) -> None:
        """Record model prediction."""
        
    def record_error(self, error: float) -> None:
        """Record prediction error."""
        
    def record_system_metrics(self, metrics: Dict[str, float]) -> None:
        """Record system metrics."""
        
    def get_statistics(self) -> Dict[str, float]:
        """Get aggregated statistics."""
        
    def get_system_health(self) -> Dict[str, Any]:
        """Get system health status."""

AlertManager

Manages system alerts and notifications.

class AlertManager:
    def set_thresholds(self, thresholds: Dict[str, float]) -> None:
        """Set alert thresholds."""
        
    def add_alert_handler(self, handler: Callable) -> None:
        """Add alert handler function."""
        
    def check_metric(self, metric: str, value: float, component: str) -> None:
        """Check metric against threshold."""
        
    def get_active_alerts(self) -> List[Dict[str, Any]]:
        """Get currently active alerts."""

PerformanceTracker

Tracks and analyzes system performance.

class PerformanceTracker:
    def set_baseline(self, baseline: Dict[str, float]) -> None:
        """Set performance baseline."""
        
    def record_metrics(self, metrics: Dict[str, float]) -> None:
        """Record performance metrics."""
        
    def get_performance_report(self) -> Dict[str, Any]:
        """Get performance analysis report."""

DeepSeek Integration

The DeepSeek module provides integration with DeepSeek AI services.

DeepSeekAPI

Main interface for DeepSeek AI services.

class DeepSeekAPI:
    def __init__(self, api_key: str):
        """Initialize DeepSeek API client."""
        
    def get_rl_interface(self) -> DeepSeekRL:
        """Get reinforcement learning interface."""
        
    def get_distill_interface(self) -> DeepSeekDistill:
        """Get model distillation interface."""

Usage Examples

For detailed usage examples, please refer to the following files in the examples/ directory:

  • monitoring_example.py: Demonstrates monitoring system usage
  • advanced_strategy.py: Shows implementation of an advanced trading strategy
  • complete_example.py: Provides a complete workflow example

Error Handling

The framework defines several custom exceptions:

  • DeepChainError: Base exception class
  • ModelError: Model-related errors
  • ValidationError: Validation failures
  • BlockchainError: Blockchain interaction errors
  • DataError: Data processing errors
  • ConfigError: Configuration errors
  • DeploymentError: Model deployment errors

Best Practices

  1. Strategy Implementation

    • Implement all abstract methods
    • Handle edge cases
    • Add proper logging
    • Include error handling

  2. Blockchain Integration

    • Always verify transactions
    • Handle network errors
    • Implement retry logic
    • Store proofs securely

  3. Edge Deployment

    • Test on target devices
    • Monitor performance
    • Handle resource constraints
    • Implement fallback logic

  4. Monitoring

    • Set appropriate thresholds
    • Configure alerts
    • Track key metrics
    • Regular health checks

Examples

See the examples/ directory for implementation examples:

  • simple_strategy.py: Basic RL strategy
  • simple_validator.py: Basic validator
  • simple_deployer.py: Basic deployer
  • usage_example.py: Complete usage example