- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic.
- Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks.
- Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations).
- Enhanced documentation for TRAMA, including performance profiles and quality metrics.
- Updated workspace configuration by removing unnecessary folder references.
- LTMA (Linear Trend Moving Average): Introduces a predictive moving average using dual cascaded EMAs for trend estimation.
- MCNMA (McNicholl EMA): Implements a zero-lag TEMA using a cascaded EMA structure for enhanced responsiveness.
- NLMA (Non-Lag Moving Average): Utilizes a damped cosine kernel to achieve reduced lag in moving averages.
- NMA (Natural Moving Average): Adapts smoothing based on volatility profiles using a square-root kernel.
- NYQMA (Nyquist Moving Average): Applies the Nyquist-Shannon theorem to prevent aliasing in cascaded moving averages.
- RAIN (Rainbow Moving Average): Combines multiple SMA layers with weighted averages for multi-scale smoothing.
- TRAMA (Trend Regularity Adaptive Moving Average): Adapts smoothing based on the frequency of new highs and lows in price data.
- Added detailed comments explaining the validation limitations for MMA and ZLEMA due to differences in algorithm implementations.
- Implemented validation tests for True Range against TALib and Tulip, ensuring directional agreement.
- Updated Ulcer Index validation to clarify differences in algorithmic approaches between QuanTAlib and Skender.
- Enhanced Ease of Movement tests to verify directional agreement with Tulip's EMV, noting differences in volume scaling.
- Expanded Klinger Volume Oscillator tests to validate against Skender and Tulip, focusing on directional agreement across multiple period configurations.
- Improved Negative Volume Index tests to compare percentage changes with Tulip, addressing differences in starting values.
- Updated Positive Volume Index tests to validate against Tulip, emphasizing percentage change comparisons.
- Enhanced Williams Accumulation/Distribution tests to verify directional agreement with Tulip, highlighting formula differences.
- Implemented ChopIndicator for Quantower with configurable period and cold value display.
- Created Chop class for calculating the Choppiness Index with detailed documentation.
- Added comprehensive unit tests for Chop functionality, covering various market conditions and edge cases.
- Developed markdown documentation for CHOP, detailing its historical context, mathematical foundation, and usage examples.
- Established a remediation plan for channel indicators documentation, identifying gaps and prioritizing updates.
codefactor-io