kingchenc
b228a70d7d
Deepens the **Moving Averages** family with seven widely-used variants (396 → 403 indicators), the first batch of Part B (family deepening). All are scalar `f64 → f64`: | Indicator | Binding | Notes | |-----------|---------|-------| | `SineWeightedMa` | `SWMA` | symmetric half-cycle sine-weighted window | | `GeometricMa` | `GMA` | rolling geometric mean (log-space average) | | `Ehma` | `EHMA` | exponential Hull MA (Hull construction over EMAs) | | `MedianMa` | `MedianMA` | rolling median, robust to single outliers | | `AdaptiveLaguerreFilter` | `AdaptiveLaguerre` | Ehlers' adaptive Laguerre filter (median-of-normalised-error γ) | | `GeneralizedDema` | `GD` | Tillson's volume-factor double EMA; `v=1` is DEMA, `v=0` is EMA | | `HoltWinters` | `HoltWinters` | Holt's linear double exponential smoothing (level + trend) | LSMA was dropped from the planned set: it already ships as `LinearRegression` (TA-Lib `LINEARREG`, the rolling least-squares endpoint). The five single-period filters use the generated scalar macro bindings; `GeneralizedDema` (period, v) and `HoltWinters` (alpha, beta) use hand-written node/python bindings with the typed wasm macro (precedent `T3` / `Alma`). Full coverage: core modules with per-branch unit tests (100% intent), mod/lib catalogue, FAMILIES group + assert, README + docs counters, CHANGELOG, all three bindings (regenerated `index.d.ts` / `index.js`), fuzz drivers, and the python/node test registries. Local verification: `cargo test -p wickra-core` (lib 3255 + doc 361), `cargo clippy --workspace --all-targets --all-features -D warnings` clean, node `npm run build && npm test` (478), python `pytest` (791).
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 WebAssembly. 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 200+ streaming-first indicators across sixteen 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.
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 four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, unsafe-forbidden Rust core.
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.