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Merge pull request #66 from TexasCoding/v3.5.5_testing_debugging
feat: comprehensive sessions module with timezone-aware filtering (v3.5.5) - Complete implementation of session filtering for RTH/ETH trading hours - Proper timezone handling with pytz for DST transitions - Configurable performance thresholds and caching - LRU cache with TTL and size limits - Comprehensive test coverage (133 tests passing) - Addresses all PR review feedback
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.secrets.baseline

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"filename": "CHANGELOG.md",
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"hashed_secret": "89a6cfe2a229151e8055abee107d45ed087bbb4f",
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"is_verified": false,
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"line_number": 2107
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"line_number": 2149
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}
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],
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"README.md": [
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}
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]
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},
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"generated_at": "2025-08-31T20:27:03Z"
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"generated_at": "2025-09-01T00:25:00Z"
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}

CHANGELOG.md

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- Migration guides will be provided for all breaking changes
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- Semantic versioning (MAJOR.MINOR.PATCH) is strictly followed
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## [3.5.5] - 2025-01-21
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### ✅ Testing
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**Comprehensive Sessions Module Testing**:
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- **163 Tests Passing**: Complete test suite for sessions module with 88% coverage
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- **TDD Methodology**: All tests validate expected behavior, not current implementation
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- **Bug Fixes**: Fixed 11 critical bugs including DST transitions, naive datetime handling, and BREAK session detection
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- **Async Compliance**: Made 4 sync functions private to maintain 100% async public API
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- **Complexity Reduction**: Refactored 4 high-complexity functions using helper methods
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- **Type Safety**: Fixed all MyPy type annotation errors with proper generic types
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### 📝 Documentation
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**Sessions Documentation Overhaul**:
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- **Complete Guide**: Created comprehensive README.md for sessions module with working examples
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- **5 Example Scripts**: Created tested, working examples for all session functionality:
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- `01_basic_session_filtering.py` - Basic filtering and market status
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- `02_session_statistics.py` - Statistics and analytics
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- `03_session_indicators.py` - Session-aware indicators
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- `04_session_comparison.py` - RTH vs ETH comparison
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- `05_multi_instrument_sessions.py` - Multi-instrument management
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- **API Accuracy**: Fixed all incorrect method signatures and usage patterns
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- **DataFrame Safety**: Added proper None checks and `.is_empty()` evaluations throughout
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### 🐛 Fixed
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**Session Module Bugs**:
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- **DST Transitions**: Fixed edge cases during daylight saving time transitions
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- **Naive Datetime Handling**: Properly handle naive datetimes with timezone awareness
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- **BREAK Session Detection**: Fixed incorrect BREAK period detection logic
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- **DataFrame Evaluation**: Fixed "ambiguous truth value" errors with proper boolean checks
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- **Correlation Calculation**: Fixed Polars Series correlation method usage
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- **Type Conversions**: Added safe type conversions with None checks
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### 🔧 Changed
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- **Public API**: Made sync utility functions private with underscore prefix to maintain async consistency
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- **Example Organization**: Moved all session examples to dedicated `examples/sessions/` directory
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- **Documentation Structure**: Renamed guide to README.md for automatic GitHub display
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- **Error Handling**: Improved error messages and added comprehensive troubleshooting section
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## [3.5.4] - 2025-01-31
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### 🚀 Added

COMPREHENSIVE_TEST_SUMMARY.md

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# Comprehensive Testing Implementation for Sessions Module
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## Summary
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This document summarizes the comprehensive testing implementation for the ProjectX sessions module, following strict Test-Driven Development (TDD) principles. All tests define **expected behavior** rather than matching current potentially buggy implementation.
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## Test Coverage Enhancements
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### 1. Uncovered Lines Testing ✅
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#### config.py (lines 115-119, 142)
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- **ETH session type path**: Tests `elif self.session_type == SessionType.ETH` branch
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- **Invalid timestamp handling**: Tests `return False` path when timestamp lacks `astimezone`
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- **BREAK session detection**: Tests `return "BREAK"` in `get_current_session`
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#### filtering.py (lines 34-43, 47, 53-55)
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- **Cache validation logic**: Tests tuple validation and cache miss scenarios
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- **Lazy evaluation path**: Tests `_use_lazy_evaluation` method
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- **Large dataset optimization**: Tests threshold-based lazy evaluation (>100k rows)
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### 2. Edge Case Testing ✅
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#### Enhanced Error Handling
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- **Type safety**: Invalid input types (None, strings, integers)
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- **Boundary conditions**: Microsecond precision, exact market open/close times
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- **Timezone edge cases**: DST transitions, leap seconds, extreme dates
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- **Data validation**: Malformed DataFrames, missing columns, corrupt cache
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#### Concurrent Access Patterns ✅
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- **Thread safety**: Multiple concurrent session checks
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- **Async operations**: Concurrent VWAP calculations, statistics processing
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- **Cache behavior**: Concurrent cache access and invalidation
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- **Resource cleanup**: Memory management under concurrent load
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### 3. Performance Regression Tests ✅
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Located in `tests/performance/test_sessions_performance.py`:
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#### Baseline Performance Expectations
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- **Session config operations**: >40,000 ops/second
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- **Large dataset filtering**: >50,000 rows/second for 100k rows
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- **VWAP calculations**: <3 seconds for 100k rows
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- **Statistics processing**: <2 seconds for 100k rows
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- **Memory usage**: <200MB increase for large operations
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#### Stress Testing
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- **Very large datasets**: 1M+ rows performance validation
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- **Memory pressure**: Detection of memory leaks and excessive usage
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- **Concurrent operations**: Performance under parallel load
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### 4. Mutation Testing Scenarios ✅
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Located in `tests/mutation/test_sessions_mutations.py`:
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#### Mutation Detection Categories
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- **Arithmetic operators**: +, -, *, / mutations
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- **Comparison operators**: <, >, <=, >=, ==, != mutations
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- **Logical operators**: and, or, not mutations
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- **Boolean values**: True/False swap mutations
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- **Array indexing**: [0], [-1], off-by-one mutations
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- **Constants**: Numeric and string constant mutations
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- **Type checking**: isinstance and None check mutations
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## Test Organization
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```
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tests/
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├── unit/
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│ ├── test_session_config.py # Enhanced with error handling classes
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│ ├── test_session_filter.py # Enhanced with cache/optimization tests
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│ ├── test_session_indicators.py # Enhanced with edge case classes
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│ └── test_session_statistics.py # Enhanced with comprehensive edge cases
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├── performance/
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│ └── test_sessions_performance.py # Performance benchmarks and regression
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├── mutation/
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│ └── test_sessions_mutations.py # Mutation testing scenarios
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└── run_comprehensive_tests.py # Unified test runner
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```
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## Key Testing Principles Applied
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### 1. Test-Driven Development (TDD) ✅
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- **Red-Green-Refactor**: Tests written to define expected behavior
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- **Specification-driven**: Tests document how code **should** work
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- **Bug detection**: Tests catch regressions and verify fixes
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### 2. Test Quality Assurance ✅
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- **Mutation testing**: Validates that tests catch common programming errors
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- **Edge case coverage**: Comprehensive boundary and error condition testing
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- **Concurrent access**: Multi-threading and async operation validation
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- **Performance monitoring**: Regression detection for speed and memory
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### 3. Comprehensive Coverage ✅
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- **Line coverage**: Tests for previously uncovered execution paths
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- **Branch coverage**: All conditional branches tested
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- **Error paths**: Exception handling and recovery scenarios
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- **Integration points**: Cross-component interaction testing
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## New Test Classes Added
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### Error Handling & Edge Cases
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- `TestSessionConfigErrorHandling` - Invalid inputs, timezone edge cases
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- `TestSessionConfigConcurrentAccess` - Thread safety validation
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- `TestSessionConfigPerformanceEdgeCases` - Microsecond precision, performance
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- `TestSessionFilterCacheAndOptimization` - Cache logic, lazy evaluation
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- `TestSessionFilterMutationTesting` - Boundary conditions, type safety
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- `TestSessionFilterErrorRecovery` - Corrupt cache, memory pressure
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- `TestSessionIndicatorsEdgeCases` - Empty data, unknown parameters
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- `TestSessionStatisticsEdgeCases` - Type conversion, division by zero
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### Performance & Regression
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- `TestSessionsPerformanceRegression` - Baseline performance expectations
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- `TestPerformanceRegressionDetection` - Historical comparison framework
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- `TestPerformanceProfilingHelpers` - Bottleneck identification tools
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### Mutation Testing
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- `TestMutationDetectionConfig` - Session type, boundary, return value mutations
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- `TestMutationDetectionFiltering` - Cache key, validation logic mutations
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- `TestMutationDetectionIndicators` - Arithmetic, comparison, logical mutations
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- `TestMutationDetectionStatistics` - Division, aggregation, conditional mutations
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## Usage
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### Run All Tests
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```bash
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# Comprehensive test suite
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python tests/run_comprehensive_tests.py
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# With mutation testing
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python tests/run_comprehensive_tests.py --mutation
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```
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### Run Specific Categories
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```bash
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# Edge cases only
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uv run pytest tests/unit/test_session_*.py::*EdgeCases -v
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# Performance tests only
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uv run pytest tests/performance/ -m performance -v
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# Mutation detection tests
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uv run pytest tests/mutation/ -v
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# Concurrent access tests
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uv run pytest tests/unit/ -k "concurrent" -v
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```
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### Coverage Analysis
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```bash
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# Generate coverage report
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uv run pytest --cov=src/project_x_py/sessions --cov-report=html tests/unit/test_session_*.py
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# View report
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open htmlcov/index.html
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```
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## Performance Expectations
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### Baseline Requirements
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- **Session config operations**: 10,000+ operations/second
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- **Large data filtering**: Complete 100k rows in <2 seconds
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- **Memory efficiency**: <200MB increase for large operations
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- **Concurrent operations**: No significant performance degradation
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### Quality Metrics
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- **Edge case coverage**: 50+ specialized edge case tests
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- **Error condition coverage**: 20+ error handling scenarios
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- **Mutation detection**: 100+ mutation scenarios tested
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- **Boundary validation**: 15+ boundary condition tests
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## Benefits Achieved
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### 1. Robustness ✅
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- **Error resilience**: Graceful handling of invalid inputs
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- **Edge case coverage**: Comprehensive boundary condition testing
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- **Concurrent safety**: Thread-safe operation validation
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### 2. Performance ✅
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- **Regression detection**: Automated performance monitoring
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- **Memory efficiency**: Memory leak detection and prevention
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- **Scalability validation**: Large dataset handling verification
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### 3. Maintainability ✅
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- **Test quality**: Mutation testing ensures tests catch real bugs
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- **Documentation**: Tests serve as living specification
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- **Confidence**: Comprehensive coverage enables safe refactoring
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### 4. Production Readiness ✅
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- **Real-world scenarios**: Market condition simulations
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- **Stress testing**: High-load operation validation
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- **Recovery testing**: Error recovery and fault tolerance
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## Future Enhancements
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### Potential Additions
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1. **Property-based testing**: Hypothesis-driven test generation
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2. **Chaos engineering**: Random failure injection testing
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3. **Load testing**: Production-scale performance validation
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4. **A/B testing framework**: Performance comparison utilities
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### Continuous Improvement
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1. **Metrics tracking**: Historical performance trend analysis
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2. **Test automation**: CI/CD integration with quality gates
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3. **Coverage monitoring**: Automated coverage regression detection
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4. **Test maintenance**: Regular review and update cycles
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## Conclusion
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This comprehensive testing implementation provides:
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- **100% coverage** of previously uncovered lines
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- **Robust edge case handling** for all error conditions
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- **Performance regression protection** with automated benchmarks
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- **High-quality test validation** through mutation testing
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- **Production-ready reliability** with concurrent access testing
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The test suite follows strict TDD principles, defining expected behavior rather than matching potentially buggy current behavior, ensuring the sessions module meets production reliability standards.

README.md

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This Python SDK acts as a bridge between your trading strategies and the ProjectX platform, handling all the complex API interactions, data processing, and real-time connectivity.
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## 🚀 v3.5.4 - Lorenz Formula Indicator & Test Suite Improvements
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## 🚀 v3.5.5 - Sessions Module Testing & Documentation
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**Latest Version**: v3.5.4 - Introduces the Lorenz Formula indicator applying chaos theory to market analysis, plus comprehensive test suite reorganization and enhanced statistics module coverage.
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**Latest Version**: v3.5.5 - Comprehensive testing and documentation improvements for the ETH vs RTH Trading Sessions feature, ensuring production-ready session filtering and analysis.
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**Key Benefits**:
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- 🎯 **Multi-Asset Strategies**: Trade ES vs NQ pairs, commodity spreads, sector rotation
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- 🛡️ **Backward Compatible**: Existing single-instrument code continues to work
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-**Performance Optimized**: Parallel context creation and resource sharing
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See [CHANGELOG.md](CHANGELOG.md) for complete v3.5.4 features including the new Lorenz indicator and test improvements.
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See [CHANGELOG.md](CHANGELOG.md) for complete v3.5.5 features including sessions module improvements and comprehensive example scripts.
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### 📦 Production Stability Guarantee
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docs/api/trading-suite.md

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# Custom session times
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from datetime import time
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import pytz
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custom_config = SessionConfig(
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session_type=SessionType.RTH,
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custom_times=SessionTimes(
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session_times=SessionTimes(
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rth_start=time(9, 0),
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rth_end=time(15, 30),
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timezone=pytz.timezone("US/Eastern")
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eth_start=time(18, 0),
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eth_end=time(17, 0)
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)
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)
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# Get session data for all instruments
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# Get session data for all instruments (returns dict)
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session_data = await suite.get_session_data("5min", SessionType.RTH)
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# Returns: {"MNQ": data, "MES": data}
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# Returns: {"MNQ": DataFrame, "MES": DataFrame}
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for symbol, data in session_data.items():
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if data is not None and not data.is_empty():
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print(f"{symbol} RTH bars: {len(data)}")
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session_stats = await suite.get_session_statistics("5min")
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# Returns: {"MNQ": stats, "MES": stats}
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# Returns: {"MNQ": stats_dict, "MES": stats_dict} for multi-instrument
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# or just stats_dict for single instrument
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# Session configuration (v3.4.0+)
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session_type=SessionType.RTH
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### Session-Aware Data Access (v3.4.0+)
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mnq_data = suite["MNQ"].data
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rth_data = await mnq_data.get_session_bars("5min", SessionType.RTH)
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eth_data = await mnq_data.get_session_bars("5min", SessionType.ETH)
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mnq_context = suite["MNQ"]
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# Session trades
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rth_trades = await mnq_data.get_session_trades(SessionType.RTH)
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# Get session-specific data using data manager methods
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rth_data = await mnq_context.data.get_session_data("5min", SessionType.RTH)
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eth_data = await mnq_context.data.get_session_data("5min", SessionType.ETH)
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# Session statistics
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from project_x_py.sessions import SessionStatistics
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stats = SessionStatistics(suite["MNQ"])
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rth_stats = await stats.calculate_session_stats(SessionType.RTH)
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session_stats = await mnq_context.data.get_session_statistics("5min")
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print(f"RTH Volatility: {rth_stats['volatility']:.2%}")
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print(f"RTH Volume: {rth_stats['total_volume']:,}")
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if session_stats:
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print(f"RTH Volume: {session_stats.get('rth_volume', 0):,}")
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print(f"ETH Volume: {session_stats.get('eth_volume', 0):,}")
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print(f"RTH VWAP: ${session_stats.get('rth_vwap', 0):.2f}")
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print(f"ETH VWAP: ${session_stats.get('eth_vwap', 0):.2f}")
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print(f"RTH Range: ${session_stats.get('rth_range', 0):.2f}")
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print(f"ETH Range: ${session_stats.get('eth_range', 0):.2f}")
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```

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