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docs: refresh the cross-library benchmark numbers

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The README and Streaming-vs-Batch benchmark tables were a stale snapshot
("5 000-bar series", numbers from an older machine). Re-run
`python -m benchmarks.compare_libraries` on the current hardware against
the same peer set (finta + talipp; TA-Lib and pandas-ta stay excluded on
Windows) and replace the tables with the fresh numbers.

The new run uses the script's current defaults: a 20 000-bar batch series
and a 5 000-bar seed + 15 000-bar live streaming workload — both more
representative of real backtests than the previous 5 000 / 2 000-bar
sizes. Wickra still wins every batch row outright (3.5× to 1 244× faster
than the nearest peer) and the streaming RSI is ~13.8× faster than
talipp's incremental implementation.
This commit is contained in:
kingchenc
2026-05-23 00:24:41 +02:00
parent 8b4a847d24
commit a707eb5d62
2 changed files with 24 additions and 24 deletions
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README.md
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@@ -57,28 +57,28 @@ Lower µs/op = faster. Wickra wins every batch category outright, and the
streaming gap widens linearly with how much history a batch-only library has
to recompute on every tick.
### Batch — single full pass over a 5 000-bar series
### Batch — single full pass over a 20 000-bar series
Reading the table: each cell shows that library's runtime, plus how many times
slower it is than Wickra in parentheses. **★** marks the winner per row.
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|------------------------|------------------------------|
| SMA(20) | **26.0 µs ★** | 295.3 µs (11.4× slower) | 1 812.8 µs (69.7× slower) |
| EMA(20) | **16.8 µs ★** | 205.5 µs (12.2× slower) | 2 534.4 µs (150.9× slower) |
| RSI(14) | **31.2 µs ★** | 714.1 µs (22.9× slower) | 3 751.7 µs (120.2× slower) |
| MACD(12, 26, 9) | **30.8 µs ★** | 359.5 µs (11.7× slower) | 11 642.2 µs (378.0× slower) |
| Bollinger(20, 2.0) | **26.7 µs ★** | 690.6 µs (25.9× slower) | 27 482.4 µs (1 030.1× slower) |
| ATR(14) | **40.6 µs ★** | 1 120.3 µs (27.6× slower) | 3 760.2 µs (92.7× slower) |
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs ★** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs ★** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs ★** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs ★** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs ★** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs ★** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### Streaming — per-tick latency after seeding with 2 000 historical bars
### Streaming — per-tick latency after seeding with 5 000 historical bars
A batch-only library has to re-run its full indicator over the entire history on
every new tick; Wickra updates state in O(1).
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.07 µs ★** | 1.16 µs (17.5× slower) |
| RSI(14) | **0.119 µs ★** | 1.644 µs (13.8× slower) |
> TA-Lib and pandas-ta are not included here because both fail to install
> cleanly on Windows without C build tooling — which is precisely the install
docs/wiki/Streaming-vs-Batch.md
+13 -13
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@@ -115,26 +115,26 @@ history just to recompute it.
The numbers below are reproduced from the project README, where
`python -m benchmarks.compare_libraries` is the source script.
### Batch — single full pass over a 5 000-bar series
### Batch — single full pass over a 20 000-bar series
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|------------------------|------------------------------|
| SMA(20) | **26.0 µs** | 295.3 µs (11.4× slower) | 1 812.8 µs (69.7× slower) |
| EMA(20) | **16.8 µs** | 205.5 µs (12.2× slower) | 2 534.4 µs (150.9× slower) |
| RSI(14) | **31.2 µs** | 714.1 µs (22.9× slower) | 3 751.7 µs (120.2× slower) |
| MACD(12, 26, 9) | **30.8 µs** | 359.5 µs (11.7× slower) | 11 642.2 µs (378.0× slower) |
| Bollinger(20, 2.0) | **26.7 µs** | 690.6 µs (25.9× slower) | 27 482.4 µs (1 030.1× slower) |
| ATR(14) | **40.6 µs** | 1 120.3 µs (27.6× slower) | 3 760.2 µs (92.7× slower) |
| Indicator | Wickra | finta | talipp |
|---------------------|---------------------|-----------------------------|-------------------------------|
| SMA(20) | **95.6 µs** | 343.5 µs (3.6× slower) | 7 640.6 µs (79.9× slower) |
| EMA(20) | **64.6 µs** | 223.1 µs (3.5× slower) | 12 160.9 µs (188.2× slower) |
| RSI(14) | **126.2 µs** | 1 107.1 µs (8.8× slower) | 15 792.2 µs (125.1× slower) |
| MACD(12, 26, 9) | **119.0 µs** | 531.8 µs (4.5× slower) | 49 788.1 µs (418.2× slower) |
| Bollinger(20, 2.0) | **105.3 µs** | 812.0 µs (7.7× slower) | 130 938.3 µs (1 243.7× slower)|
| ATR(14) | **123.5 µs** | 5 144.8 µs (41.7× slower) | 28 816.0 µs (233.4× slower) |
### Streaming — per-tick latency after seeding with 2 000 historical bars
### Streaming — per-tick latency after seeding with 5 000 historical bars
| Indicator | Wickra (per tick) | talipp (per tick) |
|-----------|---------------------|---------------------------|
| RSI(14) | **0.07 µs** | 1.16 µs (17.5× slower) |
| RSI(14) | **0.119 µs** | 1.644 µs (13.8× slower) |
The streaming gap widens linearly with how much history a batch-only library
has to recompute on every new tick; the table above is the gap at a modest
2 000-bar seed.
has to recompute on every new tick; the table above is the gap at a 5 000-bar
seed followed by 15 000 live updates.
## Practical consequences