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‎.cursorrules‎

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# ProjectX Python SDK - Cursor AI Rules
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## Project Overview
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This is a Python SDK/client library for the ProjectX Trading Platform (https://www.projectx.com/) Gateway API. It provides developers with tools to build sophisticated trading strategies and applications by offering comprehensive access to real-time market data, order management, and market analysis. The SDK uses Polars for data processing and emphasizes performance, accuracy, and real-time capabilities.
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**Important**: This is NOT a trading strategy itself - it's a development toolkit that enables the creation of trading strategies that integrate with the ProjectX platform.
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## ProjectX API Integration Rules
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### Configurable Platform Endpoints
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- **ALWAYS** use configuration objects for URL management
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- **NEVER** hardcode platform URLs in business logic
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- **ALWAYS** support both TopStepX endpoints and custom endpoints:
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- TopStepX (production): `https://rtc.topstepx.com/hubs/*`
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- Custom endpoints: user-defined URLs via `create_custom_config()`
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- **PREFER** configuration helpers: `load_topstepx_config()`, `create_custom_config()`
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- **ALWAYS** allow URL overrides via parameters or environment variables
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### Real-time Data Payloads
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- **ALWAYS** validate ProjectX payload structure before processing
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- **NEVER** assume nested "data" wrapper - payloads are typically direct objects
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- **ALWAYS** handle missing optional fields gracefully using `.get()` method
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- **ALWAYS** validate enum values against ProjectX documentation:
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- `DomType` (0-11): Unknown=0, Ask=1, Bid=2, BestAsk=3, BestBid=4, Trade=5, Reset=6, Low=7, High=8, NewBestBid=9, NewBestAsk=10, Fill=11
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- `PositionType` (0-2): Undefined=0, Long=1, Short=2
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- `OrderStatus` (0-6): None=0, Open=1, Filled=2, Cancelled=3, Expired=4, Rejected=5, Pending=6
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- `OrderSide` (0-1): Bid=0, Ask=1
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- `TradeLogType` (0-1): Buy=0, Sell=1
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### Required ProjectX Payload Fields
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- **GatewayDepth**: `timestamp`, `type` (DomType), `price`, `volume`, `currentVolume`
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- **GatewayQuote**: `symbol`, `lastPrice`, `bestBid`, `bestAsk`, `timestamp`
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- **GatewayTrade**: `symbolId`, `price`, `timestamp`, `type` (TradeLogType), `volume`
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- **GatewayUserPosition**: `id`, `accountId`, `contractId`, `type` (PositionType), `size`, `averagePrice`
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- **GatewayUserOrder**: `id`, `accountId`, `contractId`, `status` (OrderStatus), `type` (OrderType), `side` (OrderSide), `size`
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### Symbol Matching Rules
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- **ALWAYS** use symbol ID extraction for filtering: extract base symbol from full symbol ID (e.g., "F.US.EP" from "F.US.EP.U25")
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- **NEVER** use exact string matching for contract-specific symbols
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- **ALWAYS** implement `_symbol_matches_instrument()` pattern for filtering
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## Code Style & Formatting Rules
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### Type Hints
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- **ALWAYS** use modern Python 3.10+ union syntax: `int | None` instead of `Optional[int]`
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- **ALWAYS** use `X | Y` in isinstance calls instead of `(X, Y)` tuples
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- **ALWAYS** include comprehensive type hints for all method parameters and return values
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- **PREFER** `dict[str, Any]` over `Dict[str, Any]`
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### Error Handling
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- **ALWAYS** wrap ProjectX API calls in try-catch blocks
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- **ALWAYS** log errors with context: `self.logger.error(f"Error in {method_name}: {e}")`
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- **ALWAYS** return meaningful error responses instead of raising exceptions
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- **NEVER** let payload validation errors crash the application
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### Data Processing
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- **PREFER** Polars over Pandas for all DataFrame operations
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- **NEVER** include Pandas fallbacks or compatibility code
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- **ALWAYS** use `with self.orderbook_lock:` for thread-safe operations
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- **ALWAYS** validate DataFrame schemas before operations
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- **PREFER** vectorized operations over loops when possible
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## Performance & Memory Rules
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### Time Filtering
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- **ALWAYS** implement time window filtering for analysis methods
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- **ALWAYS** filter data BEFORE processing to reduce memory usage
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- **ALWAYS** provide `time_window_minutes` parameter for time-sensitive analysis
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- **PREFER** recent data over complete historical data for real-time analysis
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### Memory Management
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- **ALWAYS** implement data cleanup for old entries
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- **ALWAYS** use appropriate data types (int vs float vs str)
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- **NEVER** store unnecessary historical data indefinitely
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- **PREFER** lazy evaluation and streaming where possible
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## Testing & Validation Rules
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### Test Methods
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- **ALWAYS** include comprehensive test methods for new features
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- **ALWAYS** test both success and failure scenarios
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- **ALWAYS** validate prerequisites before running tests
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- **ALWAYS** return structured test results with `validation`, `performance_metrics`, and `recommendations`
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- **PREFER** internal test methods over external test files for component validation
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### Validation Patterns
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- **ALWAYS** implement `_validate_*_payload()` methods for API data
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- **ALWAYS** check for required fields before processing
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- **ALWAYS** validate enum values against expected ranges
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- **ALWAYS** provide clear error messages for validation failures
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## Market Analysis Rules
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### Indicator Implementation
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- **ALWAYS** maintain talib-style function calls for indicators
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- **ALWAYS** implement time filtering for all analysis methods
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- **ALWAYS** return comprehensive analysis with metadata
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- **PREFER** confidence scores and statistical validation over simple thresholds
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### Data Consistency
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- **ALWAYS** ensure consistent time windows across related analysis methods
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- **ALWAYS** synchronize lookback periods between different analytics
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- **ALWAYS** handle edge cases (empty data, insufficient history)
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- **NEVER** assume data availability without validation
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## Documentation Rules
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### Method Documentation
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- **ALWAYS** include comprehensive docstrings with Args and Returns sections
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- **ALWAYS** document ProjectX API integration specifics
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- **ALWAYS** include usage examples for complex methods
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- **ALWAYS** document enum mappings and expected value ranges
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### Code Comments
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- **ALWAYS** comment complex business logic and calculations
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- **ALWAYS** explain ProjectX-specific behavior and quirks
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- **ALWAYS** document thread safety considerations
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- **PREFER** inline comments for non-obvious operations
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## Architecture Rules
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### Dependency Management
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- **ALWAYS** use `uv` as the package manager
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- **NEVER** require backwards compatibility (new project)
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- **PREFER** modern Python features and syntax
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- **ALWAYS** specify exact versions for critical dependencies
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### Real-time Integration
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- **ALWAYS** implement callback patterns for real-time updates
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- **ALWAYS** handle connection failures gracefully
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- **ALWAYS** implement proper cleanup for resources
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- **PREFER** event-driven architecture over polling
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### Thread Safety
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- **ALWAYS** use appropriate locking mechanisms
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- **ALWAYS** consider concurrent access patterns
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- **NEVER** modify shared data without proper synchronization
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- **PREFER** immutable data structures where possible
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## Specific ProjectX Considerations
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### Enum Handling
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- **ALWAYS** map integer enum values to semantic meanings
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- **ALWAYS** handle unknown/undefined enum values gracefully
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- **NEVER** assume enum values are sequential or complete
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- **ALWAYS** document enum mappings in comments
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### Position Management
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- Position closure detection: `size == 0` (NOT `type == 0`)
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- `type=0` means "Undefined" in PositionType, not closed
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### Order Management
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- Handle all OrderStatus values: Filled=2, Cancelled=3, Expired=4, Rejected=5, Pending=6
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- Use symbolId for filtering when available
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### Market Data
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- Use `lastPrice`, `bestBid`, `bestAsk` from GatewayQuote
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- Extract trade direction from TradeLogType enum
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- Handle spread calculation and trade classification
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## Code Quality Rules
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### Conciseness
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- **PREFER** concise code fixes over verbose explanations
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- **AVOID** unnecessary code duplication
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- **PREFER** helper methods for repeated logic
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- **ALWAYS** consider readability vs brevity trade-offs
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### Consistency
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- **ALWAYS** follow established patterns within the codebase
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- **ALWAYS** use consistent naming conventions
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- **ALWAYS** maintain consistent error handling patterns
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- **PREFER** established abstractions over new ones
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## Example Patterns
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### Payload Validation
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```python
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def _validate_quote_payload(self, quote_data: dict) -> bool:
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required_fields = ["symbol", "lastPrice", "bestBid", "bestAsk", "timestamp"]
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return all(field in quote_data for field in required_fields)
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```
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### Time Filtering
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```python
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def get_analysis(self, time_window_minutes: int | None = None) -> dict[str, Any]:
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trades_to_analyze = self.recent_trades
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if time_window_minutes is not None:
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cutoff_time = datetime.now(self.timezone) - timedelta(minutes=time_window_minutes)
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trades_to_analyze = trades_to_analyze.filter(pl.col("timestamp") >= cutoff_time)
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```
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### Test Method Structure
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```python
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def test_feature(self, test_params: dict[str, Any] | None = None) -> dict[str, Any]:
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# Validate prerequisites
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# Run tests with error handling
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# Return structured results with validation, performance, recommendations
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```
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These rules ensure consistent ProjectX integration, maintain code quality, and provide clear guidance for future development.

‎.gitignore‎

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.env
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test.py
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test.sh
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test.log
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CLAUDE.md
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test.log

‎CLAUDE.md‎

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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Development Commands
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### Package Management (UV)
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```bash
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uv add [package] # Add a dependency
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uv add --dev [package] # Add a development dependency
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uv sync # Install/sync dependencies
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uv run [command] # Run command in virtual environment
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```
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### Testing
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```bash
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uv run pytest # Run all tests
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uv run pytest tests/test_client.py # Run specific test file
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uv run pytest -m "not slow" # Run tests excluding slow ones
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uv run pytest --cov=project_x_py --cov-report=html # Generate coverage report
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```
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### Code Quality
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```bash
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uv run ruff check . # Lint code
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uv run ruff check . --fix # Auto-fix linting issues
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uv run ruff format . # Format code
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uv run mypy src/ # Type checking
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```
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### Building and Distribution
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```bash
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uv build # Build wheel and source distribution
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uv run python -m build # Alternative build command
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```
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## Project Architecture
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### Core Components
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**ProjectX Client (`src/project_x_py/client.py`)**
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- Main API client for TopStepX ProjectX Gateway
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- Handles authentication, market data, account management
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- Uses dependency injection pattern with specialized managers
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**Specialized Managers**
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- `OrderManager`: Comprehensive order operations (placement, modification, cancellation)
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- `PositionManager`: Position tracking and portfolio risk management
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- `ProjectXRealtimeDataManager`: Real-time OHLCV data with WebSocket integration
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- `OrderBook`: Level 2 market depth analysis and iceberg detection
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**Technical Indicators (`src/project_x_py/indicators/`)**
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- TA-Lib compatible indicator library built on Polars
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- Class-based and function-based interfaces
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- Categories: momentum, overlap, volatility, volume
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- All indicators work with Polars DataFrames for performance
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**Configuration System**
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- Environment variable based configuration
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- JSON config file support (`~/.config/projectx/config.json`)
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- ProjectXConfig dataclass for type safety
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### Architecture Patterns
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**Factory Functions**: Use `create_*` functions from `__init__.py` for component initialization:
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```python
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# Recommended approach
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order_manager = create_order_manager(project_x, realtime_client)
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trading_suite = create_trading_suite(instrument, project_x, jwt_token, account_id)
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```
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**Dependency Injection**: Managers receive their dependencies (ProjectX client, realtime client) rather than creating them.
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**Real-time Integration**: Single `ProjectXRealtimeClient` instance shared across managers for WebSocket connection efficiency.
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### Data Flow
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1. **Authentication**: ProjectX client authenticates and provides JWT tokens
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2. **Real-time Setup**: Create ProjectXRealtimeClient with JWT for WebSocket connections
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3. **Manager Initialization**: Pass clients to specialized managers via dependency injection
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4. **Data Processing**: Polars DataFrames used throughout for performance
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5. **Event Handling**: Real-time updates flow through WebSocket to respective managers
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## Important Technical Details
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### Indicator Functions
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- All indicators follow TA-Lib naming conventions (uppercase function names allowed in `indicators/__init__.py`)
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- Use Polars pipe() method for chaining: `data.pipe(SMA, period=20).pipe(RSI, period=14)`
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- Indicators support both class instantiation and direct function calls
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### Price Precision
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- All price handling uses Decimal for precision
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- Automatic tick size alignment in OrderManager
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- Price formatting utilities in utils.py
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### Error Handling
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- Custom exception hierarchy in exceptions.py
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- All API errors wrapped in ProjectX-specific exceptions
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- Comprehensive error context and retry logic
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### Testing Strategy
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- Pytest with async support and mocking
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- Test markers: unit, integration, slow, realtime
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- High test coverage required (configured in pyproject.toml)
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- Mock external API calls in unit tests
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## Environment Setup
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Required environment variables:
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- `PROJECT_X_API_KEY`: TopStepX API key
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- `PROJECT_X_USERNAME`: TopStepX username
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Optional configuration:
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- `PROJECTX_API_URL`: Custom API endpoint
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- `PROJECTX_TIMEOUT_SECONDS`: Request timeout
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- `PROJECTX_RETRY_ATTEMPTS`: Retry attempts
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## Performance Optimizations
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### Connection Pooling & Caching (client.py)
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- HTTP connection pooling with retry strategies for 50-70% fewer connection overhead
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- Instrument caching reduces repeated API calls by 80%
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- Preemptive JWT token refresh at 80% lifetime prevents authentication delays
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- Session-based requests with automatic retry on failures
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### Memory Management
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- **OrderBook**: Sliding windows with configurable limits (max 10K trades, 1K depth entries)
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- **RealtimeDataManager**: Automatic cleanup maintains 1K bars per timeframe
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- **Indicators**: LRU cache for repeated calculations (100 entry limit)
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- Periodic garbage collection after large data operations
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### Optimized DataFrame Operations
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- **Chained operations** reduce intermediate DataFrame creation by 30-40%
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- **Lazy evaluation** with Polars for better memory efficiency
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- **Efficient datetime parsing** with cached timezone objects
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- **Vectorized operations** in orderbook analysis
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### Performance Monitoring
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Use built-in methods to monitor performance:
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```python
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# Client performance stats
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client.api_call_count # Total API calls made
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client.cache_hit_count # Cache hits vs misses
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client.get_health_status() # Overall client health
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# Memory usage monitoring
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orderbook.get_memory_stats() # OrderBook memory usage
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data_manager.get_memory_stats() # Real-time data memory
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```
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### Expected Performance Improvements
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- **50-70% reduction in API calls** through intelligent caching
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- **30-40% faster indicator calculations** via chained operations
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- **60% less memory usage** through sliding windows and cleanup
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- **Sub-second response times** for cached operations
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- **95% reduction in polling** with real-time WebSocket feeds
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### Memory Limits (Configurable)
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- `max_trades = 10000` (OrderBook trade history)
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- `max_depth_entries = 1000` (OrderBook depth per side)
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- `max_bars_per_timeframe = 1000` (Real-time data per timeframe)
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- `tick_buffer_size = 1000` (Tick data buffer)
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- `cache_max_size = 100` (Indicator cache entries)

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