Screamer

Screamingly fast rolling statistics, technical indicators, and signal filters for time series. One C++ core, two languages: a simple Python API and a JavaScript/WebAssembly build, producing identical results on batch arrays and live streams.

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Screamer runs in Python and in JavaScript. Both bind the same C++ engine, so a computation in one language matches the other to the last bit.

Why screamer

  • Fast. Every operator is implemented in C++ and routinely outruns equivalent NumPy and pandas code, often by a factor of two or more.
  • One API, batch or streaming. The same operator runs on a stored array or a live, event-driven stream and produces identical results, so code tested on historical data deploys to production unchanged.
  • Causal by construction. Output depends only on current and past inputs, never future ones, which eliminates look-ahead bias.
  • Batteries included. 200+ rolling and exponentially-weighted statistics, technical indicators (MACD, RSI, Bollinger Bands, ATR, and more), OHLC volatility estimators, signal filters, plus stream operators and composable pipelines.
  • Python and JavaScript. The same operators and the same pipeline model in both ecosystems, from one C++ source.

Python

Python 3.12 or newer.

pip install screamer

The wheel is self-contained (a single abi3 wheel per platform, no build step and no runtime dependencies). For development setup and the example notebooks see the Installation page.

Fit a line to each sliding window of 50 values, take the slope, then its sign to get the trend direction:

import numpy as np
from screamer import RollingPoly2, Sign

data = np.cumsum(np.random.normal(size=300))

slope = RollingPoly2(window_size=50, derivative_order=1)
sign = Sign()

trend = sign(slope(data))   # the same calls work on a live stream, one value at a time

JavaScript

Node 18 or newer, or any modern browser.

npm install @screamer-labs/screamer

The package is self-contained: the WebAssembly module is embedded, so there is no separate asset to configure. The WASM loads once via await ready(), then you call operators directly:

import { ready, RollingPoly2, Sign } from "@screamer-labs/screamer";
await ready();

const data = Float64Array.from({ length: 300 }, () => Math.random());

const slope = RollingPoly2(50, 1);   // window_size, derivative_order
const sign = Sign();

const trend = sign(slope(data));     // a number, a Float64Array, an iterable, or an async stream

Operators compose into pipelines:

import { ready, Input, Pipeline, RollingMean, Diff } from "@screamer-labs/screamer";
await ready();

const x = Input("x");
const y = Diff(1)(RollingMean(3)(x));      // compose operators into a graph
const p = new Pipeline([x], [y]);
p(data);                                    // bind data at call time; p.live() streams

Documentation

Contributing

Contributions are welcome. See CONTRIBUTING.md for how to set up a development environment, build the extension, run the tests, and open a pull request. By participating you agree to abide by our Code of Conduct.

License

Screamer is released under the MIT License. See LICENSE.

Project links

Data verified by PyPI on Aug 10, 2026
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2 maintainers

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Credits

Author/Maintainer: Thijs van den Berg

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.12

Provides Extra

test viz validation docs dev

Tags

streaming indicators real-time algorithmic trading low-latency

Classifiers

Report project as malware

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