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SIMD Refactor: Merge simd-dev into dev (#55)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat> Co-authored-by: Warp <agent@warp.dev>
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Claude Opus 4.5
aider
Warp
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# Forecasts
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> "Prediction is very difficult, especially about the future." Niels Bohr
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Forecasting and predictive models. Unlike reactive indicators that smooth past data, forecasts attempt to project future values. Extrapolation is inherently uncertain. Use with appropriate skepticism and position sizing.
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## Indicator Status
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| Indicator | Full Name | Status | Description |
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| :--- | :--- | :---: | :--- |
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| [AFIRMA](lib/forecasts/afirma/Afirma.md) | Adaptive FIR Moving Average | | Windowed sinc coefficients. Optimal frequency response. Can extrapolate. |
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| CFO | Chande Forecast Oscillator | =Ë | Percentage difference between price and linear regression forecast. |
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| MLP | Multilayer Perceptron | =Ë | Neural network regressor. Nonlinear pattern learning. |
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| TSF | Time Series Forecast | =Ë | Linear regression projected forward. Standard extrapolation. |
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**Status Key:** Implemented | =Ë Planned
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## Selection Guide
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| Use Case | Recommended | Why |
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| :--- | :--- | :--- |
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| Smooth extrapolation | AFIRMA | FIR with extrapolation coefficients. Configurable lookahead. |
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| Linear trend projection | TSF | Simple, interpretable. Works when trend is linear. |
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| Forecast deviation | CFO | Shows when price diverges from linear forecast. |
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| Nonlinear patterns | MLP | Neural network learns complex relationships. Requires training. |
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## Forecasting Principles
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| Aspect | Reality | Implication |
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| :--- | :--- | :--- |
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| Extrapolation risk | Markets are non-stationary | Short horizons more reliable |
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| Model uncertainty | All models are wrong | Use ensemble or confidence intervals |
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| Regime changes | Past patterns may not repeat | Monitor forecast errors |
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| Overfitting | Complex models fit noise | Prefer simple models when possible |
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Forecasting is not prediction. It is disciplined extrapolation of patterns that may or may not persist.
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