trendet - is a Python package for trend detection on stock time series data

identify_all_trends.py

import trendet
    
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = identify_all_trends(equity='bbva',
                         country='spain',
                         from_date='01/01/2018',
                         to_date='01/01/2019',
                         window_size=5)

df.reset_index(inplace=True)

with plt.style.context('paper'):
    plt.figure(figsize=(20, 10))

    ax = sns.lineplot(x=df['Date'], y=df['Close'])

    labels = df['Up Trend'].dropna().unique().tolist()

    for label in labels:
        sns.lineplot(x=df[df['Up Trend'] == label]['Date'],
                     y=df[df['Up Trend'] == label]['Close'],
                     color='green')

        ax.axvspan(df[df['Up Trend'] == label]['Date'].iloc[0],
                   df[df['Up Trend'] == label]['Date'].iloc[-1],
                   alpha=0.2,
                   color='green')

    labels = df['Down Trend'].dropna().unique().tolist()

    for label in labels:
        sns.lineplot(x=df[df['Down Trend'] == label]['Date'],
                     y=df[df['Down Trend'] == label]['Close'],
                     color='red')

        ax.axvspan(df[df['Down Trend'] == label]['Date'].iloc[0],
                   df[df['Down Trend'] == label]['Date'].iloc[-1],
                   alpha=0.2,
                   color='red')

    plt.show()

identify_df_trends.py

import trendet

import investpy

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

test = investpy.get_historical_data(equity='repsol',
                                    country='spain',
                                    from_date='01/01/2018',
                                    to_date='01/01/2019')

res = identify_df_trends(df=test, column='Close')

res.reset_index(inplace=True)

with plt.style.context('paper'):
    plt.figure(figsize=(20, 10))

    ax = sns.lineplot(x=res['Date'], y=res['Close'])

    labels = res['Up Trend'].dropna().unique().tolist()

    for label in labels:
        sns.lineplot(x=res[res['Up Trend'] == label]['Date'],
                     y=res[res['Up Trend'] == label]['Close'],
                     color='green')

        ax.axvspan(res[res['Up Trend'] == label]['Date'].iloc[0],
                   res[res['Up Trend'] == label]['Date'].iloc[-1],
                   alpha=0.2,
                   color='green')

    labels = res['Down Trend'].dropna().unique().tolist()

    for label in labels:
        sns.lineplot(x=res[res['Down Trend'] == label]['Date'],
                     y=res[res['Down Trend'] == label]['Close'],
                     color='red')

        ax.axvspan(res[res['Down Trend'] == label]['Date'].iloc[0],
                   res[res['Down Trend'] == label]['Date'].iloc[-1],
                   alpha=0.2,
                   color='red')

    plt.show()

identify_trends.py

import trendet

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = trendet.identify_trends(equity='bbva',
                             country='spain',
                             from_date='01/01/2018',
                             to_date='01/01/2019',
                             window_size=5,
                             trend_limit=3,
                             labels=['A', 'B', 'C'])

df.reset_index(inplace=True)

with plt.style.context('paper'):
    plt.figure(figsize=(20, 10))

    ax = sns.lineplot(x=df['Date'], y=df['Close'])

    values = list()

    value = {
        'trend': 'Up Trend',
        'color': 'green',
    }

    values.append(value)

    value = {
        'trend': 'Down Trend',
        'color': 'red',
    }

    values.append(value)

    for label in ['A', 'B', 'C']:
        for value in values:
            sns.lineplot(x=df[df[value['trend']] == label]['Date'],
                         y=df[df[value['trend']] == label]['Close'],
                         color=value['color'])

            ax.axvspan(df[df[value['trend']] == label]['Date'].iloc[0],
                       df[df[value['trend']] == label]['Date'].iloc[-1],
                       alpha=0.1,
                       color=value['color'])

    plt.show()

The generated plot by the identify_trends script via trendet is presented below:

The resulting plot of identify_all_trends script via trendet looks like:

And the resulting plot of identify_df_trends script via trendet looks like:

Please check trendet and STAR it if you found it useful!

NOTE
This is just the Alpha version, which just covers the basic needs and there is still a lot of work to do.