kingchenc
0479191b66
* feat(core): add signed dragonfly/gravestone encoding to Doji Doji gains an opt-in `.signed()` mode that classifies a detected Doji by the position of its body within the bar range: dragonfly (long lower shadow) emits +1.0 (bullish), gravestone (long upper shadow) emits -1.0 (bearish), and a long-legged/standard Doji emits 0.0. The default detection-flag behaviour (+1.0/0.0) is unchanged, so existing callers are unaffected. The other 14 candlestick patterns already emit the uniform +1 bull / -1 bear / 0 none convention; document that explicitly with a "Signed +-1 encoding" section on each so the whole family is a consistent drop-in ML feature. * feat(bindings): expose Doji signed mode in python, node, wasm Hand-write the Doji binding in all three language bindings (instead of the shared candle-pattern macro) so it accepts an opt-in `signed` flag and exposes an `is_signed`/`isSigned` accessor: - Python: `Doji(signed=False)` keyword argument - Node: `new Doji(signed?)` optional constructor argument (index.d.ts/.js regenerated via napi build) - WASM: `new Doji(signed?)` optional constructor argument The default construction is unchanged, so existing callers keep the direction-less +1/0 detection flag. * test(bindings,fuzz): cover Doji signed dragonfly/gravestone encoding - python: dragonfly(+1)/gravestone(-1)/neutral(0) and default-flag cases in test_known_values - node: equivalent signed/default assertions in indicators.test.js - fuzz: drive a signed Doji alongside the default in indicator_update_candle * docs: document signed candlestick convention and Doji signed mode README gains a candlestick sign-convention note; CHANGELOG records the new opt-in Doji signed dragonfly/gravestone encoding under [Unreleased].
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.