semvec/pypi-packages huggingface dataset:
custom-prediction-library 0.3.1
A custom prediction library with automated hyperparameter tuning, training utilities, exponential smoothing, and visualisation.
License: MIT
Requires Python: >=3.6


# Custom Prediction Library

A Python library for:

- Simplified model creation using Scikit-learn.
- Automated hyperparameter tuning with Optuna and Bayesian Optimization.
- Time series forecasting using Exponential Smoothing.
- Interactive visualisation with Bokeh.

## Installation

Install via pip:

```bash
pip install custom_prediction_lib
```

## Features

- **Model Factory**: Create classification and regression models with minimal setup.
- **Hyperparameter Tuning**: Includes GridSearch, RandomizedSearch, and advanced tuning with Optuna.
- **Time Series**: Exponential smoothing for forecasting.
- **Visualisation**: Interactive plots for predictions and forecasts.

## Usage

### Example 1: Model Factory

```python
from custom_prediction_lib.model_creation import ModelFactory

# Create a Random Forest classifier
model = ModelFactory.create_model(
    task="classification", 
    model_type="random_forest", 
    n_estimators=100
)
```

### Example 2: Hyperparameter Tuning

```python
from custom_prediction_lib.tuning import HyperparameterTuner
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=500, n_features=10, random_state=42)

model = RandomForestClassifier(random_state=42)
param_distributions = {
    "n_estimators": (50, 200),
    "max_depth": (5, 20),
}

best_model, best_params = HyperparameterTuner.optuna_search(
    model, X, y, param_distributions, n_trials=30, visualise=True
)
```

### Example 3: Time Series Forecasting

```python
from custom_prediction_lib.time_series import ExponentialSmoothingModel

# Example time series data
data = [100, 120, 130, 125, 135, 140, 145]

# Fit and forecast using exponential smoothing
model = ExponentialSmoothingModel(trend="add", seasonal="mul", seasonal_periods=4)
model.fit(data)
forecast = model.forecast(steps=3)
model.plot(data, forecast_steps=3)
```

## License

This project is licensed under the MIT License.
fx_backtester 1.2.1
A dashboard for backtesting FX strategies.
License: MIT
Requires Python: <4.0,>=3.11

========================
FX Strategies Backtester
========================


.. image:: https://img.shields.io/pypi/v/fx_backtester.svg
        :target: https://pypi.python.org/pypi/fx_backtester

.. image:: https://img.shields.io/travis/AdamHim1/fx_backtester.svg
        :target: https://travis-ci.com/AdamHim1/fx_backtester

.. image:: https://readthedocs.org/projects/fx-backtester/badge/?version=latest
        :target: https://fx-backtester.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status




A dashboard for backtesting FX strategies.


* Free software: MIT license
* Documentation: https://fx-backtester.readthedocs.io.


Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage