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Miha Kralj
2026-02-27 07:48:12 -08:00
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lib/trends_FIR/conv/Conv.md
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# CONV: Convolution Moving Average
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Trend (FIR MA) |
| **Inputs** | Source (close) |
| **Parameters** | double[] kernel |
| **Outputs** | Single series (Conv) |
| **Output range** | Tracks input |
| **Warmup** | `period` bars |
### TL;DR
- CONV (Convolution Moving Average) is the ultimate tool for the signal processing purist.
- Parameterized by double[] kernel.
- Output range: Tracks input.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "If you want a moving average that behaves exactly how you want it to, build it yourself. CONV is the 'Bring Your Own Kernel' of indicators."
CONV (Convolution Moving Average) is the ultimate tool for the signal processing purist. It doesn't presume to know what kind of smoothing you need; it simply asks for a kernel (a set of weights) and applies it to the data. Want a Gaussian filter? A Sinc filter? A custom edge-detection filter? CONV runs them all.
@@ -151,4 +168,4 @@ For a typical 14-period kernel: ~68 + 224 ≈ **292 bytes** per instance.
1. **Kernel Direction**: Our implementation applies the kernel such that the last element of the kernel multiplies the most recent data point. If you import kernels from other DSP libraries, you might need to reverse them.
2. **Normalization**: Kernel weights are *not* automatically normalized. If the sum of the weights is not 1.0, the output scale will be different from the input scale. This is a feature, not a bug (allows for differential filters).
3. **Performance**: A kernel size of 1000 will be 100x slower than a kernel size of 10. Use FFT-based convolution for massive kernels (not implemented here; this is for trading, not searching for extraterrestrial life).
3. **Performance**: A kernel size of 1000 will be 100x slower than a kernel size of 10. Use FFT-based convolution for massive kernels (not implemented here; this is for trading, not searching for extraterrestrial life).