Metadata-Version: 2.1
Name: tradeflow
Version: 0.0.14
Summary: A package to simulate autocorrelated time series of signs
Author-email: Martin Gangand <martin.gangand@gmail.com>
Project-URL: Homepage, https://martingangand.github.io/tradeflow/
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: <3.13,>=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib >=3.0.0
Requires-Dist: numpy >=1.21.0
Requires-Dist: pandas >=1.3.0
Requires-Dist: statsmodels >=0.12.2

# tradeflow : generate autocorrelated time series of signs

`tradeflow` lets you generate autocorrelated time series of signs.

## Installation

```
pip install tradeflow
```

## How to use
You can create an autoregressive model from a training time series of signs `time_series_signs`:

```
>>> import tradeflow
>>>
>>> ar_model = tradeflow.AR(signs=time_series_signs, max_order=50, order_selection_method='pacf', information_criterion=None)
```

To fit the model parameters, you have to call the `fit` function:

```
>>> ar_model.fit(method='yule_walker')
```

You can then easily simulate an autocorrelated time series of signs by calling the `simulate` function:
```
>>> simulated_signs = ar_model.simulate(size=15, seed=1)
>>> print(simulated_signs)
```

## Documentation

Read the full documentation [here](https://martingangand.github.io/tradeflow/).

## License

Copyright (c) 2024 Martin Gangand

Distributed under the terms of the
[MIT](https://github.com/MartinGangand/tradeflow/blob/main/LICENSE) license.
