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# Benchmarks
Read these as **relative** speedups on identical input — absolute µs depend on
CPU, memory clock and OS scheduler, not a universal contract. **Streaming is the
headline**: it is where Wickra's design pays off and where the gap is measured in
orders of magnitude, not percent. The batch numbers come second and are shown
honestly — the leanest crates edge Wickra out on the simple recurrences, and that
is a deliberate trade for warmup/NaN semantics, not a ceiling.
- **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
Rust 1.92 (release: `lto = "fat"`, `codegen-units = 1`), Python 3.12.
- **Reproduce yourself:**
- Rust core vs Rust crates: `cargo bench -p wickra-bench`
- Python vs Python libs: `pip install -e bindings/python[bench]` then
`python -m benchmarks.compare_libraries` (auto-detects installed peers).
## 1. Streaming — the structural win
Live trading feeds one tick at a time. Wickra updates every indicator in **O(1)**;
batch-only libraries (TA-Lib, tulipy, finta, pandas-ta) have no incremental API
and must recompute the whole history on every tick. Only `talipp` (Python) and
`ta-rs` / `yata` (Rust) carry real per-tick state. This is the gap the library
was built to expose.
**Python — per-tick latency** (seed 5 000 bars, then feed ticks one at a time):
| Indicator | **★ Wickra** | talipp | TA-Lib (recompute) |
|------------------|------------------:|------------------|-----------------------|
| SMA(20) | **0.063 µs ★** | 0.59 µs (9×) | 204 µs (3 300×) |
| EMA(20) | **0.060 µs ★** | 0.72 µs (12×) | 212 µs (3 500×) |
| RSI(14) | **0.065 µs ★** | 1.06 µs (16×) | 230 µs (3 600×) |
| MACD(12, 26, 9) | **0.078 µs ★** | 4.22 µs (54×) | 245 µs (3 100×) |
| Bollinger(20, 2) | **0.088 µs ★** | 5.15 µs (58×) | 229 µs (2 600×) |
Against the only other incremental Python peer Wickra is **958× faster**;
against the recompute-on-every-tick libraries it is **2 60014 000× faster**
(`finta` RSI hits 14 000×). tulipy / pandas-ta land in the same recompute band
as TA-Lib.
**Rust — per-tick latency** (whole 50 000-bar series, lower = faster):
| Indicator | **★ Wickra** | kand | ta-rs | yata |
|------------------|------------------:|-----:|------:|-----:|
| SMA(20) | 50 | 38 | 47 | 38 |
| EMA(20) | 154 | 69 | 56 | 69 |
| RSI(14) | 164 | 216 | 74 | — |
| MACD(12, 26, 9) | 275 | 143 | 66 | — |
| Bollinger(20, 2) | **128 ★** | 248 | 168 | — |
| ATR(14) | 152 | 166 | 61 | — |
`ta-rs` hands back a bare `f64` from the first tick with no warmup and no
validation; it leads several rows by giving those guarantees up. Against `kand`,
Wickra wins streaming RSI, Bollinger and ATR. `yata` exposes only SMA/EMA as
raw-value methods, so its other rows are omitted rather than faked.
## 2. Batch — competitive, not the headline
Whole series in one call. Here hand-tuned C (`tulipy`, TA-Lib) and the leanest
Rust crate (`kand`) win the simple recurrences — Wickra trades a few µs per pass
for the `None`-warmup, NaN-safety and bit-exact `batch == streaming` guarantees
none of them keep. It still wins several rows outright and beats the rest of the
field everywhere.
**Python** (20 000-bar pass, µs/op, lower = faster):
| Indicator | Wickra | TA-Lib | tulipy | pandas-ta |
|------------------|---------:|-------:|-------:|----------:|
| SMA(20) | 22.7 | **15.4** | 15.9 | 33.7 |
| EMA(20) | 30.8 | **30.3** | 31.1 | 48.8 |
| RSI(14) | 58.9 | 72.5 | **38.5** | 94.8 |
| MACD(12, 26, 9) | 71.7 | 99.1 | **33.5** | 207.6 |
| Bollinger(20, 2) | 84.9 | 65.7 | **32.3** | 336.4 |
| ATR(14) | 52.0 | 79.4 | **31.9** | — |
Wickra beats TA-Lib on RSI, MACD and ATR and the whole Python field on every
row; tulipy's SIMD C stays ahead on the heavier indicators.
**Rust** (50 000-bar pass, µs, lower = faster). Only Wickra and `kand` expose a
batch API; `ta-rs` and `yata` are streaming-only:
| Indicator | **★ Wickra** | kand |
|------------------|------------------:|-------:|
| SMA(20) | 53 | **41** |
| EMA(20) | 111 | **71** |
| RSI(14) | **221 ★** | 259 |
| MACD(12, 26, 9) | 533 | **327** |
| Bollinger(20, 2) | **404 ★** | 460 |
| ATR(14) | **122 ★** | 169 |
Run the suite yourself:
```bash
cargo bench -p wickra-bench # Rust core vs kand / ta-rs / yata
pip install -e bindings/python[bench] # Python peers
python -m benchmarks.compare_libraries
```