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factor-pricing-model-universe-2023.0.0
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Package to build universes for factor pricing model
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | factor-pricing-model-universe-2023.0.0 |
| نام | factor-pricing-model-universe |
| نسخه کتابخانه | 2023.0.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Factor Pricing Model |
| ایمیل نویسنده | factor.pricing.model@gmail.com |
| آدرس صفحه اصلی | https://github.com/factorpricingmodel/factor-pricing-model-universe |
| آدرس اینترنتی | https://pypi.org/project/factor-pricing-model-universe/ |
| مجوز | MIT |
# Factor Pricing Model Universe
<p align="center">
<a href="https://github.com/factorpricingmodel/factor-pricing-model-universe/actions?query=workflow%3ACI">
<img src="https://img.shields.io/github/workflow/status/factorpricingmodel/factor-pricing-model-universe/CI/main?label=CI&logo=github&style=flat-square" alt="CI Status" >
</a>
<a href="https://factor-pricing-model-universe.readthedocs.io">
<img src="https://img.shields.io/readthedocs/factor-pricing-model-universe.svg?logo=read-the-docs&logoColor=fff&style=flat-square" alt="Documentation Status">
</a>
<a href="https://codecov.io/gh/factorpricingmodel/factor-pricing-model-universe">
<img src="https://img.shields.io/codecov/c/github/factorpricingmodel/factor-pricing-model-universe.svg?logo=codecov&logoColor=fff&style=flat-square" alt="Test coverage percentage">
</a>
</p>
<p align="center">
<a href="https://python-poetry.org/">
<img src="https://img.shields.io/badge/packaging-poetry-299bd7?style=flat-square&logo=data:image/png;base64,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" alt="Poetry">
</a>
<a href="https://github.com/ambv/black">
<img src="https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square" alt="black">
</a>
<a href="https://github.com/pre-commit/pre-commit">
<img src="https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=flat-square" alt="pre-commit">
</a>
</p>
<p align="center">
<a href="https://pypi.org/project/factor-pricing-model-universe/">
<img src="https://img.shields.io/pypi/v/factor-pricing-model-universe.svg?logo=python&logoColor=fff&style=flat-square" alt="PyPI Version">
</a>
<img src="https://img.shields.io/pypi/pyversions/factor-pricing-model-universe.svg?style=flat-square&logo=python&logoColor=fff" alt="Supported Python versions">
<img src="https://img.shields.io/pypi/l/factor-pricing-model-universe.svg?style=flat-square" alt="License">
</p>
Package to build universes for factor pricing model. For further details, please refer
to the [documentation](https://factor-pricing-model-universe.readthedocs.io/en/latest/)
## Installation
Install this via pip (or your favourite package manager):
`pip install factor-pricing-model-universe`
## Usage
The library contains the pipelines to build the universe. You can
run the pipelines interactively in Jupyter Notebook.
```python
from fpm_universe import pipeline
```
Alternatively, for scheduled runs, you can create a configuration
and run the command line entry point to create the universe.
### Configuration
The configuration is in yaml format and contains a few inputs
| Name | Description |
| :----------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| `output_filename` | Output filename |
| `intermediate_directory` | Intermediate directory to export the pipeline outputs |
| `start_datetime` | Start datetime of the universe |
| `last_datetime` | Last datetime of the universe |
| `frequency` | Frequency of the universe. For further details, please see the "Offset aliases" in pandas [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) |
| `pipeline` | List of pipelines to filter the universe |
| `data` | Defines the data used by pipeline, or referred by yaml tag `!data` |
Each pipeline returns a pandas dataframe indicating if the instrument is included into the
universe on the specified date / time. For example, the pipeline returns the following
dataframe
```
+------------+--------+-------+
| date | AAPL | GOOGL |
+------------+--------+-------+
| 2022-11-17 | True | False |
+------------+--------+-------+
| 2022-11-18 | True | True |
+------------+--------+-------+
```
and it indicates AAPL is included in the universe on both 2022-11-17 and 2022-11-18
while GOOGL only on 2022-11-18.
By default, the pipeline functions are imported from module `fpm_universe.pipeline`.
Each data defines the method to retrieve from the source, or the operator on the
source data. The return type of each data is unconstrained. It can be a json-like dict,
a list, a pandas series, or even a pandas dataframe.
In the configuration, Each data can be referred by yaml tag `!data`, and it is loaded
in lazy only when it is referred by another data object or a pipeline.
### Command
The entry point `factor-pricing-model-universe` is to generate the universe regarding
the given configuration to the destination, with dynamically passing the parameters
to format the configuration.
The arguments of the entry point are
| Argument | Description |
| :--------------------: | :----------------------------------------------: |
| `-c, --config TEXT` | Required. Configuration file path. |
| `-p, --parameter TEXT` | Parameters to be formatted in the configuration. |
For example, given the configuration as follows,
```
output_filename: "{output_directory}/{date}.parquet"
intermediate_directory: "{output_directory}/{date}"
start_datetime: "2015-01-01"
last_datetime: "{date}"
frequency: "B"
pipeline:
- name: range_validity
function: range_validity
parameters:
values: !data initial_validity
data:
symbols:
function: jq_compile
parameters:
json_filename: "{data_directory}/index/sp500/default/{date}.json"
pattern: "[.[] | .tickers[]] | sort | unique | .[]"
initial_validity:
function: jq_compile
parameters:
json_filename: "{data_directory}/listings/{date}.json"
pattern: ".[] | {{ symbol: .symbol, valid_start_datetime: .ipoDate, valid_last_datetime: .delistingDate }}"
includes:
symbol: !data symbols
```
and run the following command
```
factor-pricing-model-universe \
--config <path> \
--parameter output_directory=$HOME/output \
--parameter data_directory=$HOME/data \
--parameter date=2022-10-20
```
the universe dataframe is output to `$HOME/output/2022-10-20.parquet`
(formatted with the parameter `output_directory` and `date`).
نیازمندی
| مقدار | نام |
|---|---|
| >=5.0,<6.0 | Sphinx |
| >=0.3.6,<0.4.0 | insipid-sphinx-theme |
| >=1.3.0,<2.0.0 | jq |
| >=0.18,<0.19 | myst-parser |
| >=1.3.5,<1.4.0 | pandas |
| >=10.0.1,<11.0.0 | pyarrow |
| >=6.0,<7.0 | pyyaml |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<4.0 | Python |
نحوه نصب
نصب پکیج whl factor-pricing-model-universe-2023.0.0:
pip install factor-pricing-model-universe-2023.0.0.whl
نصب پکیج tar.gz factor-pricing-model-universe-2023.0.0:
pip install factor-pricing-model-universe-2023.0.0.tar.gz