semvec/pypi-packages huggingface dataset:
advanced-ta 0.1.8
Python implementation of Lorentzian Classification algorithm.
Requires Python: >=3.10,<4.0

This module is a python implementation of Lorentzian Classification algorithm developed by @jdehorty in pinescript. The original work can be found here - https://www.tradingview.com/script/WhBzgfDu-Machine-Learning-Lorentzian-Classification/

## Usage

At the most simplest, you can just do this:
```python
from advanced_ta import LorentzianClassification
.
.
    # df here is the dataframe containing stock data as [['open', 'high', 'low', 'close', 'volume']]. Notice that the column names are in lower case.
    lc = LorentzianClassification(df)
    lc.dump('output/result.csv')
    lc.plot('output/result.jpg')
.
.
```

For advanced use, you can do:
```python
from advanced_ta import LorentzianClassification
from ta.volume import money_flow_index as MFI
.
.
    # df here is the dataframe containing stock data as [['open', 'high', 'low', 'close', 'volume']]. Notice that the column names are in lower case.
    lc = LorentzianClassification(
        df,
        features=[
            LorentzianClassification.Feature("RSI", 14, 2),  # f1
            LorentzianClassification.Feature("WT", 10, 11),  # f2
            LorentzianClassification.Feature("CCI", 20, 2),  # f3
            LorentzianClassification.Feature("ADX", 20, 2),  # f4
            LorentzianClassification.Feature("RSI", 9, 2),   # f5
            MFI(df['open'], df['high'], df['low'], df['close'], df['volume'], 14) #f6
        ],
        settings=LorentzianClassification.Settings(
            source='close',
            neighborsCount=8,
            maxBarsBack=2000,
            useDynamicExits=False
        ),
        filterSettings=LorentzianClassification.FilterSettings(
            useVolatilityFilter=True,
            useRegimeFilter=True,
            useAdxFilter=False,
            regimeThreshold=-0.1,
            adxThreshold=20,
            kernelFilter = LorentzianClassification.KernelFilter(
                useKernelSmoothing = False
                lookbackWindow = 8
                relativeWeight = 8.0
                regressionLevel = 25
                crossoverLag = 2
            )
        ))
    lc.dump('output/result.csv')
    lc.plot('output/result.jpg')
.
.
```

## Sample Plot

### Generated

![generated](https://bitbucket.org/lokiarya/advanced-ta/raw/c8e21204e4c6a7009141c379ec32cd05ac2d49b6/sample.jpg "Sample Generated By Library")

### Reference From TradingView

![reference](https://bitbucket.org/lokiarya/advanced-ta/raw/c8e21204e4c6a7009141c379ec32cd05ac2d49b6/reference.png "TradingView Result For Indentical Settings")


## Version History
### 0.1.8
- Replacing dependency on [TA-Lib](https://pypi.org/project/TA-Lib/) with [ta](https://pypi.org/project/ta/) to simplify setup
moonlight 0.3.1
Moonlight python wrapper
Requires Python: >=3.8,<4.0

# MoonLight ![Build Status](https://github.com/MoonLightSuite/MoonLight/actions/workflows/build.yml/badge.svg) [![codecov](https://codecov.io/gh/MoonLightSuite/MoonLight/branch/master/graph/badge.svg)](https://codecov.io/gh/MoonLightSuite/MoonLight)

MoonLight is a light-weight Java-tool for monitoring temporal, spatial and spatio-temporal properties of distributed complex systems, as *Cyber-Physical Systems* and *Collective Adaptive Systems*.

It supports the specification of properties written with the *Reach and Escape Logic* ([STREL](https://dl.acm.org/citation.cfm?id=3127050)). STREL is a linear time temporal logic, in particular, it extends the *Signal Temporal Logic*
([STL](https://link.springer.com/chapter/10.1007/978-3-642-15297-9_9)) with a number of spatial operators that permit to described complex spatial behaviors as beeing surround, reaching  target locations and escaping from specific regions.
<!-- The monitoring procedure is done with respect a single spatio-temporal trajecotry. Given a spatial configuration, a trajectory and a property the tool returns a spatio-temporal signal that describes the satisfaction of the property in each location and at each time.
The tool supports two type of semantics (satisfaction), the Boolean and the quantitative semantics.
Choosing the Boolean semantics the tool returns a Boolean satisfaction signal, that tells at each time in each location if the trajectory satisfies or not the property, choosing instead the Quantitative semantics the tool returns a real-value signal that corresponds to the value of satisfaction of the property.
-->

For more information, please visit our [Wiki](https://github.com/MoonLightSuite/MoonLight/wiki)