kingchenc
05fe7ffa90
## Summary - Dedicated batch fast paths for **EMA, RSI, Bollinger, MACD and ATR** (used by the Python bindings): one allocation filled in a single pass, warmup encoded as `NaN`, no per-element `Option` or input re-validation. Each is **bit-for-bit equal** to replaying `update` — SMA/Bollinger keep the drift-reseed cadence, the EMA-family keep the seed division and `mul_add` recurrences. Adds the `BatchNanExt` extension trait. - **Cross-library benchmark refresh**: `compare_libraries.py` reports the median across timing rounds (`--rounds` / `--streaming-rounds`), gains `--skip-batch` / `--skip-streaming`, and runs every peer through the streaming arena (recompute for batch-only libraries). `wickra-bench` drives the batch fast paths against `kand`. - **README** benchmark section reordered streaming-first (the order-of-magnitude result), with measured TA-Lib/tulipy/pandas-ta numbers in place of the CI-only placeholders. ## Impact - Python batch ~2× faster on EMA/RSI/MACD/ATR; streaming path unchanged. - The `batch == streaming` equivalence stays bit-exact. ## Verification - `cargo fmt` · `cargo clippy --workspace --all-targets --all-features -- -D warnings` (clean) - `cargo test --workspace --all-features` — 3782 unit + 420 doc tests pass - Python `pytest` — streaming-vs-batch, known-values, input-validation, smoke pass ## Notes - Node/WASM bindings keep their existing batch; the fast paths are Python-only for now.
97 lines
4.8 KiB
Markdown
97 lines
4.8 KiB
Markdown
# Benchmarks
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Read these as **relative** speedups on identical input — absolute µs depend on
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CPU, memory clock and OS scheduler, not a universal contract. **Streaming is the
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headline**: it is where Wickra's design pays off and where the gap is measured in
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orders of magnitude, not percent. The batch numbers come second and are shown
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honestly — the leanest crates edge Wickra out on the simple recurrences, and that
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is a deliberate trade for warmup/NaN semantics, not a ceiling.
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- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
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Rust 1.92 (release: `lto = "fat"`, `codegen-units = 1`), Python 3.12.
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- **Reproduce yourself:**
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- Rust core vs Rust crates: `cargo bench -p wickra-bench`
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- Python vs Python libs: `pip install -e bindings/python[bench]` then
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`python -m benchmarks.compare_libraries` (auto-detects installed peers).
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## 1. Streaming — the structural win
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Live trading feeds one tick at a time. Wickra updates every indicator in **O(1)**;
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batch-only libraries (TA-Lib, tulipy, finta, pandas-ta) have no incremental API
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and must recompute the whole history on every tick. Only `talipp` (Python) and
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`ta-rs` / `yata` (Rust) carry real per-tick state. This is the gap the library
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was built to expose.
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**Python — per-tick latency** (seed 5 000 bars, then feed ticks one at a time):
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| Indicator | **★ Wickra** | talipp | TA-Lib (recompute) |
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|------------------|------------------:|------------------|-----------------------|
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| SMA(20) | **0.063 µs ★** | 0.59 µs (9×) | 204 µs (3 300×) |
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| EMA(20) | **0.060 µs ★** | 0.72 µs (12×) | 212 µs (3 500×) |
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| RSI(14) | **0.065 µs ★** | 1.06 µs (16×) | 230 µs (3 600×) |
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| MACD(12, 26, 9) | **0.078 µs ★** | 4.22 µs (54×) | 245 µs (3 100×) |
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| Bollinger(20, 2) | **0.088 µs ★** | 5.15 µs (58×) | 229 µs (2 600×) |
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Against the only other incremental Python peer Wickra is **9–58× faster**;
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against the recompute-on-every-tick libraries it is **2 600–14 000× faster**
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(`finta` RSI hits 14 000×). tulipy / pandas-ta land in the same recompute band
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as TA-Lib.
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**Rust — per-tick latency** (whole 50 000-bar series, lower = faster):
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| Indicator | **★ Wickra** | kand | ta-rs | yata |
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|------------------|------------------:|-----:|------:|-----:|
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| SMA(20) | 50 | 38 | 47 | 38 |
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| EMA(20) | 154 | 69 | 56 | 69 |
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| RSI(14) | 164 | 216 | 74 | — |
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| MACD(12, 26, 9) | 275 | 143 | 66 | — |
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| Bollinger(20, 2) | **128 ★** | 248 | 168 | — |
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| ATR(14) | 152 | 166 | 61 | — |
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`ta-rs` hands back a bare `f64` from the first tick with no warmup and no
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validation; it leads several rows by giving those guarantees up. Against `kand`,
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Wickra wins streaming RSI, Bollinger and ATR. `yata` exposes only SMA/EMA as
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raw-value methods, so its other rows are omitted rather than faked.
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## 2. Batch — competitive, not the headline
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Whole series in one call. Here hand-tuned C (`tulipy`, TA-Lib) and the leanest
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Rust crate (`kand`) win the simple recurrences — Wickra trades a few µs per pass
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for the `None`-warmup, NaN-safety and bit-exact `batch == streaming` guarantees
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none of them keep. It still wins several rows outright and beats the rest of the
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field everywhere.
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**Python** (20 000-bar pass, µs/op, lower = faster):
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| Indicator | Wickra | TA-Lib | tulipy | pandas-ta |
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|------------------|---------:|-------:|-------:|----------:|
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| SMA(20) | 22.7 | **15.4** | 15.9 | 33.7 |
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| EMA(20) | 30.8 | **30.3** | 31.1 | 48.8 |
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| RSI(14) | 58.9 | 72.5 | **38.5** | 94.8 |
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| MACD(12, 26, 9) | 71.7 | 99.1 | **33.5** | 207.6 |
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| Bollinger(20, 2) | 84.9 | 65.7 | **32.3** | 336.4 |
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| ATR(14) | 52.0 | 79.4 | **31.9** | — |
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Wickra beats TA-Lib on RSI, MACD and ATR and the whole Python field on every
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row; tulipy's SIMD C stays ahead on the heavier indicators.
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**Rust** (50 000-bar pass, µs, lower = faster). Only Wickra and `kand` expose a
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batch API; `ta-rs` and `yata` are streaming-only:
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| Indicator | **★ Wickra** | kand |
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|------------------|------------------:|-------:|
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| SMA(20) | 53 | **41** |
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| EMA(20) | 111 | **71** |
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| RSI(14) | **221 ★** | 259 |
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| MACD(12, 26, 9) | 533 | **327** |
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| Bollinger(20, 2) | **404 ★** | 460 |
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| ATR(14) | **122 ★** | 169 |
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Run the suite yourself:
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```bash
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cargo bench -p wickra-bench # Rust core vs kand / ta-rs / yata
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pip install -e bindings/python[bench] # Python peers
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python -m benchmarks.compare_libraries
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```
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