kingchenc
d43bc9ddf3
* feat: add doji-star, gap, high-wave and hikkake candlestick patterns Five patterns, all `Input = Candle`, `Output = f64`: - Evening Doji Star (CDLEVENINGDOJISTAR) — bearish top reversal: long white bar, a doji gapping up, then a black bar closing deep into the first body; -1 (penetration configurable, default 0.3). - Morning Doji Star (CDLMORNINGDOJISTAR) — bullish bottom reversal mirror; +1. - Gap Side-by-Side White (CDLGAPSIDESIDEWHITE) — two similar white candles opening side by side after a gap, a continuation; gap up +1, gap down -1. - High-Wave (CDLHIGHWAVE) — a small body with very long shadows on both sides, an extreme indecision flag; +1 on detection. - Hikkake (CDLHIKKAKE) — an inside bar followed by a failed breakout (a trap); bullish +1, bearish -1. Counter 259 -> 264 (mod-count == lib counted block; FAMILIES total 254 -> 259). * chore: sync indicator count to 264 --------- Co-authored-by: wickra-bot <wickra-bot@users.noreply.github.com>
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.