kingchenc
34c097aee2
The published Python benchmark tables (README/BENCHMARKS.md) were a stale, incoherent run. Re-measured locally with the current build (wickra 0.6.5, post batch fast-paths) via `compare_libraries.py` on the same 9950X. - **Streaming vs talipp:** 11-56x (was 9-58x). - **Batch:** real per-indicator numbers; MACD and ATR were notably off in the old table. - **Prose:** Wickra beats TA-Lib on RSI and ATR (no longer MACD, which now trails 130 vs 111 us). Rust tables unchanged. Numbers are a single coherent run; absolute us still depend on machine state (caveat already in the doc).
97 lines
4.9 KiB
Markdown
97 lines
4.9 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) | **0.089 µs ★** | 0.96 µs (11×) | 422 µs (4 700×) |
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| EMA(20) | **0.111 µs ★** | 1.19 µs (11×) | 430 µs (3 900×) |
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| RSI(14) | **0.061 µs ★** | 0.95 µs (16×) | 298 µs (4 900×) |
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| MACD(12, 26, 9) | **0.079 µs ★** | 3.30 µs (42×) | 327 µs (4 100×) |
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| Bollinger(20, 2) | **0.089 µs ★** | 4.97 µs (56×) | 296 µs (3 300×) |
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Against the only other incremental Python peer Wickra is **11–56× faster**;
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against the recompute-on-every-tick libraries it is **2 800–19 000× faster**
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(`finta` RSI hits 19 000×). 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 | finta |
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|------------------|---------:|---------:|---------:|----------:|---------:|
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| SMA(20) | 22.2 | **15.6** | 15.9 | 32.7 | 290.1 |
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| EMA(20) | 30.5 | **30.4** | 30.9 | 46.7 | 198.5 |
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| RSI(14) | 52.3 | 72.0 | **34.2** | 88.8 | 812.3 |
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| MACD(12, 26, 9) | 129.8 | 111.1 | **38.4** | 286.8 | 716.7 |
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| Bollinger(20, 2) | 87.2 | 74.6 | **37.9** | 474.3 | 1255.5 |
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| ATR(14) | 74.7 | 87.3 | **35.5** | — | 3496.4 |
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Wickra beats pandas-ta and finta on every row and TA-Lib on RSI and ATR;
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tulipy's SIMD C (and TA-Lib on SMA/EMA) lead the remaining rows.
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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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