kingchenc
bca61322b5
Adds nine risk/performance metrics to the existing **Risk / Performance** family, all consuming a per-period return series (`f64` in, `f64` out). Indicator count **498 → 507**. ## Indicators Single-param (`new(period)`, macro bindings): - **SterlingRatio** — mean return over average drawdown of the equity curve. - **BurkeRatio** — return over root-sum-squared drawdowns. - **MartinRatio** — Ulcer Performance Index; return over RMS percentage drawdown. - **TailRatio** — 95th percentile over the absolute 5th percentile return. - **KRatio** — Kestner; equity-curve OLS slope over the standard error of that slope. - **CommonSenseRatio** — tail ratio times gain-to-pain. - **GainToPainRatio** — sum of returns over the sum of absolute losses. Multi-param (hand-written Python/Node bindings, variadic WASM macro): - **UpsidePotentialRatio** — `new(period, mar)`; upside mean over downside deviation (Sortino philosophy). - **M2Measure** — `new(period, risk_free, benchmark_stddev)`; Modigliani M², Sharpe rescaled into benchmark return units. ## Touchpoints Core modules + unit tests, `mod.rs`/`lib.rs` wiring, Python/Node/WASM bindings (`index.d.ts`/`index.js` regenerated), fuzz drive lines, Python `SCALAR` registry + Node factories, CHANGELOG, and the indicator counters. ## Verification - `cargo test -p wickra-core --lib` — 4149 passed - `cargo test -p wickra-core --doc` — 457 passed - `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean - `npm test` (node) — 577 passed - `pytest` (python) — 947 passed
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.