| Name | screamer JSON |
| Version |
0.1.32
JSON |
| download |
| home_page | None |
| Summary | Screamingly fast financial technical indicators with C++ performance and Python simplicity, built for both time series analysis and real-time, event-driven streaming. |
| upload_time | 2024-10-20 22:15:54 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License |
| keywords |
streaming
indicators
real-time
algorithmic
trading
low-latency
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Screamer
Screamingly fast financial technical indicators with C++ performance and Python simplicity, built for both time series analysis and real-time, event-driven streaming.
[](https://github.com/quantfinlib/screamer/blob/main/LICENSE)

[](https://github.com/quantfinlib/screamer/actions/workflows/ci.yml)
[](https://screamer.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/screamer/)
Screamer is a high-performance Python library designed for efficient streaming indicator algorithms. Built with a core of optimized C++ code and integrated through Python bindings, Screamer delivers lightning-fast computations for real-time data processing. The library is perfect for real-time algorithmic trading applications that need low-latency indicators.
## Installation
```bash
pip install screamer
```
## Usage
### Streaming interface
The streaming interface in screamer is designed for online stream processing, where data is processed element-by-element as it arrives, rather than working on the entire dataset at once. This is particularly useful when you’re dealing with real-time data, such as sensor readings, financial tick data, or other time-sensitive streams, where decisions or transformations need to be applied immediately as new data points become available.
In the example, we create a lag operator lag = screamer.lag(2, 0). This creates a lag stream with a delay of 2 and an initial value of 0. As new values are streamed into lag, it returns the value from 2 steps earlier.
```
import screamer
lag = screamer.lag(2, 0)
for x in [1,2,3,4]:
print(lag(x))
---
0
0
1
2
```
### Transform interface
The transform interface in screamer is designed for processing a batch of data all at once, which can sometimes be more convenient and faster than processing one value at a time. Instead of working on each element individually in a loop, the entire array of values is passed to the transform function, and it applies the lag operation internally. This can lead to a speedup since the processing is done inside optimized C++ code rather than a Python loop.
In the example, we create the same lag operator as before, lag = screamer.lag(2, 0). This operator introduces a delay of 2 steps, just like in the streaming interface. However, instead of processing each value one by one, we pass a whole array of values (x = [1, 2, 3, 4]) to the lag.transform() function:
```
import screamer
import numpy as np
lag = screamer.lag(2, 0)
x = np.arange(1, 5)
y = lag.transform(x)
print('x =', x)
print('y =', y)
---
x = [1 2 3 4]
y = [0 0 1 2]
```
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"description": "# Screamer\n\nScreamingly fast financial technical indicators with C++ performance and Python simplicity, built for both time series analysis and real-time, event-driven streaming.\n\n[](https://github.com/quantfinlib/screamer/blob/main/LICENSE)\n\n[](https://github.com/quantfinlib/screamer/actions/workflows/ci.yml)\n[](https://screamer.readthedocs.io/en/latest/?badge=latest) \n[](https://pypi.org/project/screamer/)\n\nScreamer is a high-performance Python library designed for efficient streaming indicator algorithms. Built with a core of optimized C++ code and integrated through Python bindings, Screamer delivers lightning-fast computations for real-time data processing. The library is perfect for real-time algorithmic trading applications that need low-latency indicators.\n\n## Installation\n\n```bash\npip install screamer\n```\n\n## Usage\n\n### Streaming interface \n\nThe streaming interface in screamer is designed for online stream processing, where data is processed element-by-element as it arrives, rather than working on the entire dataset at once. This is particularly useful when you\u2019re dealing with real-time data, such as sensor readings, financial tick data, or other time-sensitive streams, where decisions or transformations need to be applied immediately as new data points become available.\n\nIn the example, we create a lag operator lag = screamer.lag(2, 0). This creates a lag stream with a delay of 2 and an initial value of 0. As new values are streamed into lag, it returns the value from 2 steps earlier.\n\n```\nimport screamer\n\nlag = screamer.lag(2, 0)\n\nfor x in [1,2,3,4]:\n print(lag(x))\n\n---\n0\n0\n1\n2\n```\n\n### Transform interface\n\nThe transform interface in screamer is designed for processing a batch of data all at once, which can sometimes be more convenient and faster than processing one value at a time. Instead of working on each element individually in a loop, the entire array of values is passed to the transform function, and it applies the lag operation internally. This can lead to a speedup since the processing is done inside optimized C++ code rather than a Python loop.\n\nIn the example, we create the same lag operator as before, lag = screamer.lag(2, 0). This operator introduces a delay of 2 steps, just like in the streaming interface. However, instead of processing each value one by one, we pass a whole array of values (x = [1, 2, 3, 4]) to the lag.transform() function:\n\n```\nimport screamer\nimport numpy as np\n\nlag = screamer.lag(2, 0)\n\nx = np.arange(1, 5)\ny = lag.transform(x)\n\nprint('x =', x)\nprint('y =', y)\n---\nx = [1 2 3 4]\ny = [0 0 1 2]\n```\n",
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