Lazy rl #41
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Lazy rl #41
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…d print error messages
Make app host configurable
- Revert pytest dependency to previous minor version in requirements.txt - Ensure compatibility with existing project dependencies
- Refactor ADX, ATR, Bollinger Bands Width, and Ichimoku Cloud indicators - Implement Numba-accelerated core calculation functions - Improve computational efficiency using Numba's JIT compilation - Maintain consistent interface and return types
- Implement Numba-accelerated EMA calculation function - Replace vectorized implementation with loop-based Numba implementation - Improve computational efficiency for Exponential Moving Average calculation - Maintain consistent function interface and return types
- Implement Numba-accelerated RSI calculation function - Replace vectorized implementation with loop-based Numba implementation - Improve computational efficiency for Relative Strength Index calculation - Maintain consistent function interface and return types
- Implement Numba-accelerated Keltner Channel calculation function - Refactor ATR calculation with Numba-optimized implementation - Improve computational efficiency for Keltner Channel indicator - Maintain consistent function interface and return types
- Implement Numba-accelerated KAMA (Kaufman Adaptive Moving Average) calculation function - Replace vectorized implementation with loop-based Numba implementation - Improve computational efficiency for KAMA indicator - Maintain consistent function interface and return types
- Implement Numba-accelerated Bollinger Bands calculation function - Replace moving standard deviation with efficient Numba-optimized implementation - Simplify function parameters and improve computational efficiency - Maintain consistent function interface and return types
- Implement Numba-accelerated EMA calculation function for TRIX indicator - Replace vectorized implementation with efficient Numba-optimized implementation - Improve computational efficiency for TRIX calculation - Maintain consistent function interface and return types
- Update version number in setup.py and version.py - Increment patch version for minor release or bug fix
- Implement Numba-accelerated calculation functions for ADOSC, Aroon Oscillator, CCI, CFO, Chande, CHOP, CMO, DEMA, DTI, MACD, and Supertrend indicators - Replace vectorized and NumPy-based implementations with efficient loop-based Numba implementations - Improve computational efficiency for various technical indicators - Maintain consistent function interfaces and return types
- Update version number in setup.py and version.py - Increment patch version for minor release or bug fix
… the function also cache and move imports to modules
- Update multiple indicators to handle VWMA (matype 24) and VWAP (matype 29) - Add specific handling for these moving average types in indicators like APO, Bollinger Bands, ERI, Keltner, MAB, PPO, VLMA, and ZScore - Raise ValueError for indicators that cannot use VWMA or VWAP (KDJ, RVI, Stochastic, etc.) - Ensure consistent error handling and method calls for these special moving average types
- Add pytest test cases for indicators that do not support VWMA (matype 24) and VWAP (matype 29) - Implement error checks for kaufmanstop, kdj, rvi, stoch, and stochf indicators - Verify that ValueError is raised when unsupported moving average types are used - Ensure comprehensive test coverage for moving average type validation
- Implement Numba-accelerated EMA calculation for EFI indicator - Refactor Wavetrend indicator with Numba-optimized EMA and SMA functions - Improve computational efficiency for both indicators - Simplify and optimize calculation methods using Numba's JIT compilation
- Implement Numba-accelerated calculation functions for ADXR, Bollinger Bands Width, DX, EMA, KAMA, Linear Regression, RSI, SAR, Stochastic RSI, T3, TEMA, and VPCI indicators - Add @njit(cache=True) decorator to improve compilation and performance - Refactor and optimize various technical indicators using Numba's JIT compilation - Improve computational efficiency and maintain consistent function interfaces
…ementation - Rewrite ADXR indicator calculation with more precise DX and ADX computation - Optimize DX indicator by separating directional movement and true range calculations - Improve computational efficiency and numerical stability - Use RMA function for smoother indicator calculations - Enhance Numba JIT compilation for better performance
- Update version number in setup.py and version.py - Increment patch version for minor release or bug fix
directly call `timeframe_to_one_minute` dictionary instead of calling…
- Enhance error message clarity for order submission failures - Provide more actionable guidance about leverage and order size - Clarify the specific margin constraint preventing order execution
- Improve wording to make the error message more precise and actionable - Clarify the specific margin constraint preventing order execution - Enhance readability of the error message for users
- Move store import statements inside functions to prevent circular imports - Improve module initialization by avoiding top-level store imports - Maintain existing functionality while resolving potential import dependencies
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