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https://github.com/izikeros/trend_classifier

Library for automated signal segmentation, trend classification and analysis.
https://github.com/izikeros/trend_classifier

algorithmic-trading algotrading timeseries-segmentation trading-bot trend-analysis trend-detection

Last synced: about 1 month ago
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Library for automated signal segmentation, trend classification and analysis.

Host: GitHub
URL: https://github.com/izikeros/trend_classifier
Owner: izikeros
License: mit
Created: 2022-08-30T14:57:00.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2024-03-25T19:26:47.000Z (6 months ago)
Last Synced: 2024-05-22T00:18:55.342Z (4 months ago)
Topics: algorithmic-trading, algotrading, timeseries-segmentation, trading-bot, trend-analysis, trend-detection
Language: Python
Homepage:
Size: 640 KB
Stars: 21
Watchers: 1
Forks: 6
Open Issues: 3
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE

Awesome Lists containing this project

README

        # Trend classifier

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Library for automated signal segmentation, trend classification and analysis.

## Installation

1. The package is pip-installable. To install it, run:

   ```sh

   pip3 install trend-classifier

   ```

## Usage

### Pandas DataFrame Input

usage:

```python

import yfinance as yf

from trend_classifier import Segmenter

# download data from yahoo finance

df = yf.download("AAPL", start="2018-09-15", end="2022-09-05", interval="1d", progress=False)

x_in = list(range(0, len(df.index.tolist()), 1))

y_in = df["Adj Close"].tolist()

seg = Segmenter(x_in, y_in, n=20)

seg.calculate_segments()

```

For graphical output use `Segmenter.plot_segments()`:

```python

seg.plot_segments()

```

![Segmentation example](https://github.com/izikeros/trend_classifier/blob/main/img/screenshoot_1.jpg?raw=true)

After calling method `Segmenter.calculate_segments()` segments are identified and information is stored in `Segmenter.segments` as list of Segment objects. Each Segment object. Each Segment object has attributes such as 'start', 'stop' - range of indices for the extracted segment, slope and many more attributes that might be helpful for further analysis.

Exemplary info on one segment:

```python

from devtools import debug

debug(seg.segments[3])

```

and you should see something like this:

```

    seg.segments[3]: Segment(

        start=154,

        stop=177,

        slope=-0.37934038908585044,

        offset=109.54630934894907,

        slopes=[

            -0.45173184100846725,

            -0.22564684358754555,

            0.15555037018051593,

            0.34801127785130714,

        ],

        offsets=[

            121.65628807526804,

            83.56079272220015,

            17.32660986821478,

            -17.86417581658647,

        ],

        slopes_std=0.31334199799377654,

        offsets_std=54.60900279722876,

        std=0.933497081795997,

        span=82.0,

        reason_for_new_segment='offset',

    )

```

export results to tabular format (pandas DataFrame):

```python

seg.segments.to_dataframe()

```

![](https://github.com/izikeros/trend_classifier/blob/main/img/to_dataframe.jpg?raw=true)

(**NOTE:** for clarity reasons, not all columns are shown in the screenshot above)

## Alternative approach

- Smooth out the price data using the Savitzky-Golay filter,

- label the highs and lows.

- higher highs and higher lows indicates an uptrend.

The requirement here is than you need OHLC data for the assets you would like to analyse.

## License

[MIT](LICENSE) © [Krystian Safjan](https://safjan.com/).