Screamer

Screamingly fast rolling statistics, technical indicators, and signal filters for time series. C++ performance with a simple Python API, and identical results on batch NumPy arrays and live streams.

License Python Versions tests Docs PyPI

pip install screamer

Why screamer

  • Fast. Every function 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 function runs on a stored NumPy 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. 150+ 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.

Quick example

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

Documentation

Full documentation, the function reference grouped by topic, and runnable example notebooks live at screamer.readthedocs.io.

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 Jul 22, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (2.5.0)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for mjvakili from gravatar.com mjvakili Avatar for sitmo from gravatar.com sitmo

Credits

Author/Maintainer: Thijs van den Berg

GitHub Statistics

Data verified by PyPI on Jul 22, 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 >=3.11

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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