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).
## 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.
