mirror of
https://github.com/chainstacklabs/pumpfun-bonkfun-bot.git
synced 2026-07-27 15:27:44 +00:00
smypmsa
140 lines
5.0 KiB
Plaintext
140 lines
5.0 KiB
Plaintext
---
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alwaysApply: true
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---
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# Project Architecture Rules
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## Directory Structure
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### Package Organization
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Maintain clear separation of concerns:
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```
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src/
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├── __init__.py
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├── bot_runner.py # Main entry point
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├── config_loader.py # Configuration management
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├── core/ # Core blockchain functionality
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│ ├── client.py # Solana RPC client abstraction
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│ ├── wallet.py # Wallet operations
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│ └── priority_fee/ # Fee management
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├── platforms/ # Platform-specific implementations
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│ ├── pumpfun/ # pump.fun specific code
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│ └── letsbonk/ # letsbonk.fun specific code
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├── trading/ # Trading logic
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│ ├── base.py # Base trading classes
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│ ├── universal_trader.py # Platform-agnostic trader
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│ └── position.py # Position management
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├── monitoring/ # Event listening and monitoring
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│ ├── base_listener.py # Base listener interface
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│ └── universal_*_listener.py # Specific listeners
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├── interfaces/ # Abstract base classes
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└── utils/ # Utilities and helpers
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├── logger.py # Logging utilities
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└── idl_manager.py # IDL management
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```
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### File Naming Conventions
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- Use snake_case for all Python files and directories
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- Prefix abstract base classes with "Base" or put in `interfaces/`
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- Use "Universal" prefix for platform-agnostic implementations
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- Group related functionality in subdirectories
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## Design Patterns
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### Platform Abstraction
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Implement platform-specific functionality using the factory pattern:
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```python
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# interfaces/core.py - Define abstract interfaces
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from abc import ABC, abstractmethod
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class AddressProvider(ABC):
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@abstractmethod
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def get_program_address(self) -> str:
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pass
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# platforms/pumpfun/address_provider.py - Concrete implementation
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class PumpFunAddressProvider(AddressProvider):
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def get_program_address(self) -> str:
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return "6EF8rrecthR5Dkzon8Nwu78hRvfCKubJ14M5uBEwF6P"
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```
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### Universal Components
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Create platform-agnostic wrappers that delegate to platform-specific implementations:
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```python
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class UniversalTrader:
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def __init__(self, platform: Platform, **kwargs):
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self.platform = platform
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self.platform_trader = self._create_platform_trader()
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def _create_platform_trader(self):
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# Factory method to create platform-specific trader
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pass
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```
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### Configuration Management
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- Use YAML files for bot configurations in `bots/` directory
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- Support environment variable interpolation with `${VARIABLE}` syntax
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- Validate configurations before starting bots
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- Separate environment-specific settings in `.env` files
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## Module Dependencies
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### Import Rules
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- Core modules should not import from trading or monitoring
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- Platform-specific modules should only import from their own package and core/interfaces
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- Avoid circular imports between packages
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- Use dependency injection for cross-package dependencies
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### Dependency Layers (from low to high level)
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1. **utils/** - Utilities and helpers (no business logic dependencies)
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2. **interfaces/** - Abstract base classes and protocols
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3. **core/** - Blockchain and infrastructure (depends on utils, interfaces)
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4. **platforms/** - Platform implementations (depends on core, interfaces)
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5. **trading/** - Trading logic (depends on core, platforms, interfaces)
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6. **monitoring/** - Event listening (depends on core, platforms, interfaces)
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7. **bot_runner.py** - Main orchestrator (depends on all layers)
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## Async Architecture
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### Event Loop Management
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- Use uvloop for better performance
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- Set event loop policy at application startup
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- Use asyncio.create_task() for concurrent operations
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- Implement proper cleanup on shutdown
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### Connection Management
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- Use connection pooling for HTTP clients
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- Implement reconnection logic for WebSocket connections
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- Cache expensive resources (blockhash, account info)
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- Use async context managers for resource cleanup
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## Testing Strategy
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### Test Organization
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- Use `learning-examples/` for integration testing and validation
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- Test platform-specific components independently
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- Mock external dependencies (RPC calls, WebSocket connections)
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- Validate configurations with actual bot startup
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### Test Data
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- Use test networks for development
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- Never test with real funds or production keys
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- Create fixtures for common test scenarios
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- Document test account requirements
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## Performance Considerations
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### Caching Strategy
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- Cache recent blockhash in background task
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- Cache account information where appropriate
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- Use local caching for IDL data
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- Implement TTL for cached data
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### Resource Management
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- Limit concurrent operations based on RPC provider limits
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- Implement backoff strategies for failed requests
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- Use separate processes for production bot instances
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- Monitor memory usage in long-running processes |