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Miha Kralj
51 lines
3.0 KiB
Markdown
51 lines
3.0 KiB
Markdown
# ALMA: Benchmark Analysis
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This analysis evaluates the Arnaud Legoux Moving Average (ALMA) across four core benchmarks: accuracy, timeliness, overshooting, and smoothness. These benchmarks provide a comprehensive view of ALMA's performance characteristics and serve as a basis for comparison with other moving averages.
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## Accuracy (closeness to the original data)
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ALMA generally exhibits good accuracy in representing the original price data due to its Gaussian distribution-based weighting system.
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- **Strengths**:
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- The Gaussian distribution weighting helps to reduce noise while preserving important price trends.
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- The offset parameter allows for fine-tuning of the balance between recent and historical data representation.
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- **Considerations**:
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- Accuracy can vary based on parameter settings. Incorrect parameter selection might lead to over-smoothing or under-smoothing, potentially reducing accuracy.
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- In highly volatile markets, ALMA may sacrifice some accuracy for smoothness, especially if the sigma parameter is set to prioritize noise reduction.
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## Timeliness (amount of lag)
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ALMA is designed to minimize lag, which is one of its key advantages over traditional moving averages.
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- **Strengths**:
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- The offset parameter allows ALMA to be more responsive to recent price changes, potentially reducing lag.
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- The ability to adjust the window size provides flexibility in balancing timeliness and stability.
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- **Considerations**:
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- While ALMA generally has less lag than traditional MAs, it's not entirely lag-free. Some minimal lag may still be present, especially with larger window sizes.
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- The amount of lag can be influenced by parameter settings. Optimizing for minimal lag might come at the cost of increased noise sensitivity.
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## Overshooting (overcompensation during reversals)
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ALMA's design helps to mitigate overshooting during price reversals, but the extent can vary based on settings and market conditions.
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- **Strengths**:
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- The Gaussian distribution weighting helps to dampen extreme price movements, reducing the likelihood of significant overshooting.
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- The sigma parameter allows for control over the smoothness of transitions, potentially minimizing overshoot.
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- **Considerations**:
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- Overshooting can still occur, especially in markets with sudden, sharp reversals.
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- The degree of overshooting can be influenced by parameter settings. More aggressive settings (lower sigma, higher offset) might increase responsiveness but also the risk of overshooting.
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## Smoothness (continuous 2nd derivative, less jagged flow)
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ALMA generally produces a smoother line than many traditional moving averages, which is one of its defining characteristics.
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- **Strengths**:
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- The Gaussian distribution weighting effectively smooths out minor price fluctuations and noise.
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- The sigma parameter provides direct control over the smoothness of the line.
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- The resulting smooth line can make trend identification easier.
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- **Considerations**:
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- The degree of smoothness can be adjusted through parameter settings. |