Screamer
A fast C++ library for researching and trading time series: rolling statistics, technical indicators, filters, order-flow measures, costed backtests of single assets, portfolios, and pairs, and significance tests. One C++ core, two languages: a Python API and a JavaScript/WebAssembly build.
Screamer runs in Python and in JavaScript. Both bind the same C++ engine, so results agree to within floating-point rounding, and seeded randomization gives identical draws in both.
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, 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.
- Broad coverage. 250+ operators: rolling and exponentially weighted statistics, technical indicators (MACD, RSI, Bollinger Bands, ATR, and more), OHLC volatility estimators, signal filters, market microstructure, stream operators, and composable pipelines.
- Backtesting and validation. Costed backtests on prices, bars, trades, or quotes, with slippage, fill timing, stops, and funding; portfolios of many assets from one account; pairs and cross-sectional tools; and randomization tests, the deflated Sharpe ratio, and walk-forward evaluation to tell skill from luck.
- 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 quadratic to each sliding window of 50 values, take its slope at the latest value, then the sign of the slope 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 Chrome 95, Firefox 100 or Safari 15.2 or newer. The WebAssembly module uses native WebAssembly exceptions, which older browsers lack.
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
- Start here: Your first strategy goes from price data to a costed backtest and a p-value in one page.
- Python: guides, recipes, runnable notebooks, and the full reference grouped by topic live at screamer.readthedocs.io.
- JavaScript: see
js/README.mdfor install and the API. JavaScript API reference and guide: screamer-labs.github.io/screamer. - AI agents:
python -m screamer.docs search <keywords>andshow <Name>describe every function of the installed version, andpip install "screamer[mcp]"adds an MCP server. See Using screamer with AI agents.
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.
Release files for screamer 3.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| screamer-3.4.0.tar.gz | 9.1 MB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| screamer-3.4.0-cp312-abi3-win_amd64.whl | CPython 3.12 | abi3 | Windows x86-64 | Details |
| screamer-3.4.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | abi3 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| screamer-3.4.0-cp312-abi3-macosx_11_0_arm64.whl | CPython 3.12 | abi3 | macOS 11.0+ ARM64 | Details |
| screamer-3.4.0-cp312-abi3-macosx_10_13_x86_64.whl | CPython 3.12 | abi3 | macOS 10.13+ x86-64 | Details |
Total release size: 16.5 MB
Release files / screamer-3.4.0.tar.gz
| Download URL | screamer-3.4.0.tar.gz |
|---|---|
| Size | 9.1 MB |
| Tags | Source |
|
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| Download URL | screamer-3.4.0-cp312-abi3-win_amd64.whl |
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| Tags | CPython 3.12 Windows x86-64 abi3 |
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| Tags | CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 abi3 |
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| Tags | CPython 3.12 abi3 macOS 11.0+ ARM64 |
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|---|---|
| Size | 1.9 MB |
| Tags | CPython 3.12 abi3 macOS 10.13+ x86-64 |
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