kingchenc
c096943bdf
Completes expansion-roadmap block **A2 — Market Breadth**: the 14 indicators that remained after the `AdvanceDecline` bootstrap, all built on the existing `CrossSection` input. ## Indicators (all scalar `Indicator<Input = CrossSection, Output = f64>`) | Indicator | Reading | |-----------|---------| | `AdvanceDeclineRatio` | advancers / decliners | | `AdVolumeLine` | cumulative net advancing volume | | `McClellanOscillator` | 19/39 EMAs of ratio-adjusted net advances | | `McClellanSummationIndex` | running total of the oscillator | | `Trin` (Arms Index) | A/D ratio over up/down volume ratio | | `BreadthThrust` (Zweig) | SMA of the advancing-issues share | | `NewHighsNewLows` | new highs − new lows | | `HighLowIndex` | SMA of the record-high percent | | `PercentAboveMa` | % of the universe above its MA | | `UpDownVolumeRatio` | advancing / declining volume | | `BullishPercentIndex` | % on a point-and-figure buy signal | | `CumulativeVolumeIndex` | volume-normalised cumulative net advancing volume | | `AbsoluteBreadthIndex` | \|advancers − decliners\| | | `TickIndex` | instantaneous net advancers − decliners | ## Input model `AdVolumeLine` and `CumulativeVolumeIndex` are kept distinct (the latter normalises each tick's net advancing volume by total volume, so it stays comparable across volume regimes). `PercentAboveMa` and `BullishPercentIndex` need a per-symbol state signal that `Member` did not carry, so `Member` gains two additive flags (`above_ma`, `on_buy_signal`) via a new `Member::with_signals` constructor; the 4-arg `Member::new` leaves both cleared, so every existing caller and binding is unchanged. `CrossSection` gains volume / new-extreme / state aggregation helpers. ## Wiring Fully wired across the Rust core, the python/node/wasm bindings, the cross-section fuzz target, the README + docs indicator counters (325 → 339), and dedicated python/node streaming-vs-batch tests. `fmt` / `test --workspace --all-features` / `clippy --workspace -D warnings` / node build+test / pytest all green locally.
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 the PolyForm Noncommercial License 1.0.0. Personal projects, research, education, non-profits, and hobby trading bots are all fine; the one thing not allowed is commercial sale of the software or of services built around it. See LICENSE.