معرفی شرکت ها
fintuna-0.1.5
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 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
Parameter optimization for finance
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fintuna-0.1.5 |
| نام | fintuna |
| نسخه کتابخانه | 0.1.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Cortecs <office@cortecs.ai> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/fintuna/ |
| مجوز | - |
.. image:: https://github.com/markoarnauto/fintuna/blob/main/docs/source/images/fintuna-logo.png?raw=true
:alt: Fintuna Logo
:width: 700
Fintuna: Parameter optimization for finance
============================================
**Fintuna** is a framework that uses machine learning for asset management. It enables fast prototyping for multi-asset applications such as stock-picking.
Features:
* model training
* hyper-parameters tuning
* walk-forward backtesting
* strategy evaluation
It is a lightweight framework that combines `LightGBM <https://lightgbm.readthedocs.io>`_, `Optuna <https://optuna.readthedocs.io>`_, `Quantstats <https://github.com/ranaroussi/quantstats>`_ and `Shap <https://shap.readthedocs.io>`_ to develop ML-based stock-picking strategies.
..
Check out the section for further information, including
how to the project.
Multi Asset
------------
Looking at multiple assets is supposed to reveal more alpha-opportunities than looking at a single one. Also,
the more assets the more data which is beneficial for machine learning tasks.
Therefore, *Fintuna* is designed for multi-asset applications. The data structure
is a `Pandas Multiindex Dataframe <https://pandas.pydata.org/docs/user_guide/advanced.html#multiindex-advanced-indexing>`_ where the index is time, the first column-level is the asset and the second is the feature (= panel or longitudinal data).
Internally features are stacked and a model is trained to learn cross-asset patterns.
===== ======== ======== ========= ======== ======== ======== ========= ========
# Asset 1 Asset 2 Asset 3 Asset 4
----- ------------------ ------------------ ------------------ ------------------
# feature1 feature2 feature1 feature2 feature1 feature2 feature1 feature2
===== ======== ======== ========= ======== ======== ======== ========= ========
t0 float category float category float category float NaN
t1 float category float category float category float NaN
===== ======== ======== ========= ======== ======== ======== ========= ========
Strategy Agnostic
------------------
Fintuna is not tied to one specific trading strategy. Strategies are implemented as `fintuna.model.ModelBase`.
It defines the classification task (= `extract_label`)
as well as a a classification-to-returns mapping (= `realized_returns`).
A simple example is to predict the directional change and buy the asset with the
most confident prediction (see `fintuna.model.LongOnly`).
Backtesting
------------
Fintuna uses walk-forward backtesting.
* Train data is used to train the classifier.
* Tune data is used for hyper-parameter optimization.
* Eval data is used for backtesting
Executing the `fintuna.Finstudy.explore` method multiple times on same data introduces the risk of overfitting.
**Use feature importance and shap values, rather than merely looking at trading performance.**
.. image:: https://github.com/markoarnauto/fintuna/blob/main/docs/source/images/backtesting.png?raw=true
:alt: Walk-Forward Backtesting
Calling `fintuna.Finstudy.finish` prepares the model for deployment. It sub-selects models that also perform well on evaluation data.
and refits them on all data.
Data First
------------
A good trading strategy demands good and possibly unique data.
Fintuna does **NOT** help you in finding the right data. But consider the following guidelines:
* Have at least a few hundreds of observations.
* Use multiple assets.
* Use assets with similar characteristics (e.g. cryptos, tech-stocks, etc.).
* Make sure features across assets have similar properties (otherwise use zscore).
* Use lagged features to boost performance.
Usage
=======
Install fintuna via pip.
.. code:: bash
pip install fintuna
Run the most basic example below. For detailed guidance look at examples at `docs <https://markoarnauto.github.io/fintuna/examples.html>`_ or at `docs/source/examples`.
.. code:: python
import fintuna as ft
# get data
data, specs = ft.data.get_crypto_features()
# explore
crypto_study = ft.FinStudy(ft.model.LongOnly, data, data_specs=specs)
results = crypto_study.explore(n_trials=50, ensemble_size=3)
# analyze
ft.utils.plot_backtest(results['performance'], results['benchmark'])
TODO
-----
* Binance Trading Sink
* MajorityVoteEnsemble
* Backtest plots with shap values
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.2.3 | pandas |
| ~=1.20.0 | numpy |
| ==0.24.1 | scikit-learn |
| ==3.7.0 | APScheduler |
| ==3.2.1 | lightgbm |
| ==0.0.46 | QuantStats |
| ~=3.4.3 | matplotlib |
| ~=3.0.0 | optuna |
| ~=3.7.0 | tables |
| ~=0.40.0 | shap |
| ~=1.0.0 | python-binance |
| ~=5.2.0 | sphinx |
| ~=1.0.0 | sphinx-rtd-theme |
| ~=0.17.1 | myst-nb |
| ~=3.2.0 | sphinx-toolbox |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fintuna-0.1.5:
pip install fintuna-0.1.5.whl
نصب پکیج tar.gz fintuna-0.1.5:
pip install fintuna-0.1.5.tar.gz