kingchenc
82d7479011
* fix(core): de-duplicate 3 indicators by correcting their definitions Behavioral audit found these computed identically to another indicator: - AverageDrawdown was the mean per-bar under-water fraction = PainIndex. Now the conventional average drawdown: mean of the maximum depths of the distinct drawdown episodes in the window. - IntradayIntensity was a cumulative line = the A/D Line (Adl); its normalized form is the Chaikin Money Flow (Cmf). Now the raw per-bar Bostian intensity volume*(2c-h-l)/(h-l), distinct from both. - AwesomeOscillatorHistogram was AO - SMA(AO, n) = AcceleratorOscillator. Now the AO momentum AO[t] - AO[t-lookback] (the histogram delta); the 3rd parameter is reinterpreted from sma_period to lookback (default 1). Constructor signatures are unchanged, so the bindings keep their API. Core unit tests rewritten with the new reference values; workspace tests + clippy green. Binding value-tests and deep-dive docs are updated separately. * fix(core): redefine AdOscillator as the A/D Oscillator (was a Wad duplicate) AdOscillator computed the cumulative volume-free Williams A/D line, identical to the Wad indicator. Redefine it as the Williams A/D *Oscillator*: the same line minus its 13-bar SMA, so it oscillates around zero (mean-reverting) while Wad stays the drifting cumulative line for divergence analysis. The canonical name AdOscillator is now accurate; the trait name() becomes "ADOSC". Constructor stays no-arg (internal 13-bar signal). Unit tests rewritten and cross-checked against Wad - SMA(Wad, 13). The native bindings' "WilliamsAD" alias is renamed to "ADOSC" separately. * fix(bindings): rename WilliamsAD alias to ADOSC and update value tests Follows the core de-duplication: the native bindings exposed the Williams A/D line as 'WilliamsAD', which is now the A/D Oscillator. Rename the Python / Node.js / WASM alias to 'ADOSC' (regenerated node index.js / index.d.ts) and update the binding value-tests for the four redefined indicators (AverageDrawdown episode mean, AwesomeOscillatorHistogram momentum warmup, the Wad-line reference test now uses ta.Wad()). Python suite and node suite both pass (pytest all green, node 584/584). * docs: record indicator de-duplication in README and CHANGELOG README volume family: 'Williams A/D' -> 'Williams A/D Oscillator', 'Intraday Intensity Index' -> 'Intraday Intensity'. CHANGELOG [Unreleased] documents the four redefinitions and the native WilliamsAD -> ADOSC rename as breaking. * test(core): cover Default impl and drop dead match arm Codecov flagged AdOscillator::default() (never exercised) and the unreachable _ => panic!() arm in the AwesomeOscillatorHistogram test. Exercise Default in the accessors test and rewrite the histogram check as an if-let, removing the dead arm.
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.