kingchenc
fd9f4c8bc6
* feat(bindings): expose name() on every indicator in Node, WASM, and Python Surface the core Indicator::name() / BarBuilder::name() accessor through the three native bindings so every indicator reports its canonical name at runtime, matching the existing reset/isReady/warmupPeriod surface. - Node (napi): name(): string on all 514 classes (regenerated index.d.ts) - WASM (wasm-bindgen): name(): string on all 514 classes - Python (pyo3): name() -> str on all classes * feat(bindings): expose name() across the C ABI and C/C++/Go/C#/Java/R Regenerate the C ABI and the four generated language bindings from the updated ScriptHelpers generators so every indicator and bar builder reports its canonical name at runtime, completing name() coverage across all 10 languages. - C ABI (bindings/c): wickra_<ind>_name() -> *const c_char for all 514, cached in a per-function OnceLock<CString> with ind.name() as the source of truth; cbindgen header regenerated and vendored into bindings/go/include. - Go: Name() string; C#: string Name(); Java: String name(); R: name() S3 generic over the wk_<ind>_name C glue (methods.R + NAMESPACE). The Java regeneration also restores two fixes that had drifted out of the generator (bool* arrays via boolSegment; uint8_t ctor args cast to byte) and C# re-emits '#nullable enable'; these are no-op vs the previous committed output apart from the new name() accessors. * test(golden): pin canonical name() across all 10 language bindings Add a cross-language name() consistency check: every indicator must report the exact core Indicator::name() (which can differ from the registered class name, e.g. ChaikinMoneyFlow -> "CMF", Donchian -> "DonchianChannels"). The 514 core names are committed as testdata/golden/names.json (keyed by Rust canonical) and asserted by each binding's golden replay, which already reconstructs the whole catalogue: - node / wasm: assert against names.json in the existing golden test - python: new test_golden_names.py over the shared node manifest - go / csharp / java / c+c++ / r: the golden-test generators load names.json and emit a name assertion per indicator (regenerated test artifacts committed) All 10 bindings return identical names by construction (each delegates to core), so this pins that contract and guards against a future binding breaking the passthrough. * docs(changelog): record name() across all 10 bindings under Unreleased * fix(r): restore bool* flag marshalling in the regenerated C glue The name() regeneration had reverted the cross-section bool fix: the R glue emitted (bool *)REAL(x) for const bool* inputs, reinterpreting 8-byte doubles as 1-byte bools so every flag read as false (PercentAboveMa, NewHighsNewLows, HighLowIndex, BullishPercentIndex returned 0 instead of the breadth value). The wk_bool_vec() helper is restored in the generator and the glue routes bool arrays through it again.
Wickra — Python
Streaming-first technical indicators for Python. pip install wickra — no
system dependencies, no C build tooling.
Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js and WASM, plus a C ABI for C, C++, C#, Go, Java, R and any other C-capable language. Every indicator is an O(1) streaming state machine, so live trading bots and historical backtests share the exact same implementation. This package is the Python binding (PyO3); it exposes all 514 streaming-first indicators across twenty-four families.
Install
pip install wickra
Pre-built wheels ship for Linux, macOS, and Windows — there is nothing to compile and no C library to track down.
Quick start
import numpy as np
import wickra as ta
# Batch: classic TA-Lib-style usage over a whole array.
prices = np.linspace(100, 200, 1000)
rsi = ta.RSI(14)
values = rsi.batch(prices) # numpy array, NaN during warmup
# Streaming: the same indicator, fed tick by tick in O(1).
rsi = ta.RSI(14)
for price in live_feed:
value = rsi.update(price) # no recomputation over history
if value is not None and value > 70:
print("overbought")
batch(prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite.
Benchmark
Two benchmarks ship with the binding:
benchmarks/throughput.py— streaming and batch updates-per-second forSMA,ATRandMACD. This is per-binding FFI overhead (the same Rust core runs under every binding), not a cross-library ratio.benchmarks/compare_libraries.py— the cross-library comparison against TA-Lib, pandas-ta, tulipy and finta that backs the headline speedups.
maturin develop --release
python -m benchmarks.throughput
python -m benchmarks.compare_libraries # cross-library; auto-detects installed peers
See the repository BENCHMARKS.md.
Documentation
The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:
- Repository & full indicator list: https://github.com/wickra-lib/wickra
- Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
- Runnable examples:
examples/python/
Wickra ships native bindings for Python, Node.js, WASM and Rust, plus a
C ABI hub that any C-capable language (C, C++, C#, Go, Java, R) links against —
all exposing the same indicators from the shared, unsafe-forbidden Rust core.
Security
Found a security issue? Please don't open a public issue. Report it privately
via the affected repository's Security tab ("Report a vulnerability") or email
support@wickra.org with a subject line starting [wickra security]. Full
policy: https://github.com/wickra-lib/wickra/blob/main/SECURITY.md.
Disclaimer
Wickra is an indicator toolkit, not a trading system. The values it computes are deterministic transforms of the input data — they are not financial advice and do not predict the market. Any use in a live trading context is at your own risk. The library is provided as is, without warranty of any kind.
License
Licensed under either of Apache-2.0 or MIT at your option.