QuanTAlib/lib/trends/conv/Conv.md

CONV: Convolution Moving Average

"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.

Historical Context

Convolution is the fundamental operation of digital signal processing (DSP). While traders were busy inventing "new" moving averages by tweaking alpha values, engineers were using convolution to process audio, images, and radar signals for decades. CONV brings this raw power to financial time series, allowing for arbitrary FIR (Finite Impulse Response) filtering.

Architecture & Physics

CONV applies a sliding dot product between the data window and your custom kernel. The "physics" are entirely defined by the kernel you provide.

• Symmetric Kernel: Zero phase shift (if centered correctly).
• Asymmetric Kernel: Introduces lag or lead.
• Positive Weights: Smoothing.
• Mixed Weights: Differentiation or band-pass filtering.

Mathematical Foundation

The value at time t is the sum of the element-wise product of the kernel K and the price vector P:

\text{CONV}_t = \sum_{i=0}^{N-1} P_{t-i} \cdot K_i

Where:

• N is the length of the kernel.
• K_0 multiplies the most recent price (or oldest, depending on convention; the QuanTAlib implementation aligns K_0 with the oldest data in the window and K_{N-1} with the newest).

Performance Profile

Performance depends linearly on the kernel length (N).

Metric	Score	Notes
Throughput	★★★☆☆	O(N) kernel convolution per bar.
Allocations	★★★★★	0 bytes; hot path is allocation-free.
Complexity	★★★☆☆	O(N) window iteration required.
Precision	★★★★★	double precision.

Zero-Allocation Design

CONV stores the kernel in a pre-allocated array. The Update method performs a dot product using a circular buffer for the price history, requiring no new allocations.

Validation

Validation is performed by reproducing standard moving averages (SMA, WMA, TRIMA) using their equivalent kernels and comparing against external libraries.

Library	Status	Notes
QuanTAlib	✅	Validated against internal SMA, WMA, TRIMA.
Skender	✅	Validated against WMA (using WMA kernel).
TA-Lib	✅	Validated against WMA (using WMA kernel).
Tulip	✅	Validated against WMA (using WMA kernel).
Ooples	✅	Validated against WMA (using WMA kernel).

Common Pitfalls

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).