perf: bit-exact batch fast paths + streaming-first benchmark docs (#202)

## 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.

CHANGELOG.md

@@ -11,6 +11,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - **Andrews Pitchfork** — median line and two parallels projected from the last three swing pivots (`ANDREWS_PITCHFORK`).
 - **Murrey Math Lines** — T. H. Murrey's eighths grid over the recent trading range, each level acting as support/resistance (`MURREY_MATH_LINES`).
 - **Central Pivot Range** — the classic pivot flanked by two central levels gauging the day's expected character (`CENTRAL_PIVOT_RANGE`).
+- **Faster scalar batch paths** — `Ema`, `Rsi`, `BollingerBands`, `MacdIndicator` and `Atr` gained dedicated batch fast paths (used by the Python bindings) that strip per-element `Option`/validation overhead and the intermediate `Vec<Option<_>>` allocation, while staying *bit-for-bit* equal to replaying `update` (including the SMA/Bollinger drift-reseed). Python batch is ~2× faster on EMA/RSI/MACD/ATR; streaming is unchanged.
+- **Cross-library benchmark refresh** — `benchmarks/compare_libraries.py` now measures the median across timing rounds (`--rounds` / `--streaming-rounds`), adds `--skip-batch` / `--skip-streaming`, and drives every peer through the streaming arena (recompute for batch-only libraries). `wickra-bench` compares the batch fast paths against `kand`.
 
 ## [0.6.5] - 2026-06-07
 - **Autocorrelation Periodogram** — Ehlers autocorrelation periodogram: dominant cycle period estimate (`AUTOCORRPGRAM`).