## 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.
## Summary
An honest, tiered cross-library benchmark — and the optimization pass it triggered.
### Performance (wickra-core, outputs unchanged)
Profiling against the other Rust TA crates exposed real inefficiencies. Each
benchmarked indicator is now **5–79% faster** in both streaming and batch:
- **SMA, Bollinger**: flat `Box<[f64]>` ring buffers replace `VecDeque` (−69…79%).
- **RSI**: `100·ag/(ag+al)` collapses three divisions into one; Wilder smoothing
hoists `1/period` out of the hot path (−46%).
- **ATR**: reciprocal hoisted (−42%).
- **EMA/RSI/ATR**: per-tick `Option<f64>` hot state → bare `f64` + ready flag.
Net result vs `kand`: Wickra now wins **RSI, Bollinger and ATR** (streaming), and
ties `ta-rs` on SMA — up from losing every indicator 1.5–6× before.
### Benchmark harness
New `crates/wickra-bench` (publish=false): a Criterion benchmark comparing Wickra
against `kand`, `ta-rs` and `yata` on an identical BTCUSDT candle series, in
streaming and batch modes. Peer APIs were verified against their source, not
guessed. Wired into the nightly `cross-library-bench` workflow as a separate job.
### Honest README
The benchmark section is rewritten into three layered tables (Rust core vs Rust
crates; Python vs the Python ecosystem) that **show the losses as well as the
wins**. The "only library that combines…" claim is gone; the new framing is
breadth + multi-language reach + the deliberate safety trade-off that costs raw
speed. Added an origin/why-slower rationale and a star CTA.
### Python benchmark
Added `tulipy` runners and expanded per-tick streaming coverage to SMA/EMA/RSI/
MACD/Bollinger. `bench.in`/`bench.txt` now lock `TA-Lib` + `tulipy` (hash-pinned);
`pandas-ta` stays out (it requires Python ≥ 3.12, the bench runs on 3.11).
### Notes
- TA-Lib/tulipy numbers in the README Python table are marked ⧗ — they are
produced by the CI Linux job (C extensions don't build cleanly on every
desktop), not measured locally.
- The matching `wickra-docs` prose update is committed separately and will be
pushed with the release, per the docs-don't-lead-the-registries rule.
Verified locally: `cargo fmt`, `cargo test --workspace --all-features` (3413 core
+ bindings), `cargo clippy --workspace --all-targets --all-features -D warnings`,
Node build + 498 tests, and pytest all green.
A multi-language technical analysis library: 25 indicators across trend,
momentum, volatility, and volume families, every one a state machine with
O(1) per-tick updates. Batch evaluation is provided by a blanket extension
trait over the streaming primitive, so live trading bots and historical
backtests run the same code path.
What ships in this initial drop:
crates/wickra-core - 25 indicators, Indicator/BatchExt/Chain traits,
OHLCV types with validation; 171 unit tests,
property tests, Wilder/Bollinger textbook tests.
crates/wickra - top-level facade + criterion benches for every
indicator at 1K/10K/100K series sizes.
crates/wickra-data - streaming CSV reader, tick-to-candle aggregator,
multi-timeframe resampler, Binance Spot kline
WebSocket adapter behind feature live-binance;
11 unit + 1 doctest.
bindings/python - PyO3 + maturin, NumPy I/O, type stubs (.pyi),
56 pytest tests including streaming==batch
equivalence, Wilder reference values, lifecycle.
bindings/node - napi-rs native module, TypeScript .d.ts
auto-generated, 7 node --test cases.
bindings/wasm - wasm-bindgen ES module for browser/bundler/Node;
interactive HTML demo at examples/index.html.
examples/ - Python and Rust scripts: backtest, live trading,
parallel multi-asset, multi-timeframe, Binance.
benchmarks/ - cross-library comparison against TA-Lib,
pandas-ta, finta, talipp; Wickra wins every
category by 11-1030x (batch) and 17x+ streaming.
.github/workflows/ - CI matrix (Rust + Python + Node + WASM on
Linux/macOS/Windows), release pipeline for
PyPI wheels and npm.
Indicators (25):
Trend SMA EMA WMA DEMA TEMA HMA KAMA
Momentum RSI MACD Stochastic CCI ROC WilliamsR ADX MFI TRIX
AwesomeOscillator Aroon
Volatility BollingerBands ATR Keltner Donchian PSAR
Volume OBV VWAP (cumulative + rolling)
cargo clippy --workspace --all-targets -D warnings is clean. License: Apache-2.0.
kingchenc