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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
a7b7207801
- Updated mathematical foundations and performance profiles where necessary to maintain clarity and coherence.
50 lines
2.1 KiB
Markdown
50 lines
2.1 KiB
Markdown
# WMA: Weighted Moving Average
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> "Because yesterday matters more than last Tuesday. WMA is the linear answer to the question: 'What have you done for me lately?'"
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The Weighted Moving Average (WMA) assigns a linearly decreasing weight to data points. The most recent price gets weight $N$, the one before it $N-1$, down to 1. This makes it more responsive to recent price changes than an SMA, but without the infinite tail of an EMA.
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## Historical Context
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WMA is the "finite impulse response" (FIR) counterpart to the EMA. It was developed to reduce the lag of the SMA while maintaining a finite window of influence.
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## Architecture & Physics
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A naive WMA implementation is $O(N)$, requiring a full loop over the history window for every update. QuanTAlib uses a dual running-sum algorithm to achieve $O(1)$ complexity.
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### The O(1) Algorithm
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Two sums are maintained:
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1. `Sum`: The simple sum of values (like SMA).
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2. `WSum`: The weighted sum.
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$$ WSum_{new} = WSum_{old} - Sum_{old} + (N \times Price_{new}) $$
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$$ Sum_{new} = Sum_{old} - Price_{oldest} + Price_{new} $$
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This allows calculating a WMA(1000) as fast as a WMA(10).
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### SIMD Optimization
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For batch processing, `Wma.Batch` uses advanced vectorization (AVX2/AVX-512/Neon). It computes prefix sums and weighted updates in parallel, achieving throughputs that scalar code cannot touch.
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## Mathematical Foundation
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### 1. The Formula
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$$ WMA = \frac{\sum_{i=0}^{N-1} (N-i) \times P_{t-i}}{\frac{N(N+1)}{2}} $$
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The denominator is the sum of the weights (triangular number).
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## Performance Profile
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## Validation
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Validated against TA-Lib (`TA_WMA`) and Skender.Stock.Indicators.
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### Common Pitfalls
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1. **Drift**: Like SMA, the O(1) algorithm is susceptible to floating-point drift. QuanTAlib resets the sums every 10,000 ticks to guarantee accuracy.
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2. **Aggressiveness**: WMA reacts faster than SMA but can be "twitchy." It is often used as a component in other indicators (e.g., HMA) rather than a standalone trend filter.
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3. **Weights**: Users sometimes confuse WMA (linear weights) with EMA (exponential weights) or VWAP (volume weights).
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