Usage

As trendet is intended to be combined with investpy, the main functionality is to detect trends on stock time series data so to analyse the market and which behaviour does it have in certain date ranges. Anyways, trendet can also be used with any custom pandas.DataFrame.

Identify Custom Trends of investpy DataFrames

In the example presented below, the identify_trends function will be used to detect 3 bearish/bullish trends with a time window above 5 days, which implies that every bearish (decreasing) trend with a longer duration than 5 days will be identified and so on added to a pandas.DataFrame which already contains OHLC values, in new columns called Up Trend and Down Trend which will be labeled as specified, with letters from A to Z by default.

import trendet

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = trendet.identify_trends(stock='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=0.2, color=value['color'])

    plt.show()

So on, the resulting plot which will be outputted from the previous block of code will look like:

Identify All Trends of investpy DataFrame

Additionally trendet allows the user to identify/detect all the up and down trends on the market via the function identify_all_trends which has been included in 0.7 release. So on, the sample code for its usage is as follows:

import trendet

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = trendet.identify_all_trends(stock='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()

Which as described before, plots all the trends identified on the specified stock time series data removing overlapped trends keeping just the longer trend as minor trends are ignored. So the output of the previous block of code on trendet usage is the following plot:

Identify Trends of Custom DataFrame

Anyways, you can also use trendet for custom any pandas.DataFrame even though it is intended to be used combined with investpy. So on, via using identify_df_trends() function the trends from the specified pandas.DataFrame can be identified, just specifying the column from where the trends wants to be identified. In the example proposed below, an investpy pandas.DataFrame is being used, but you can use any other pandas.DataFrame which matches the specified conditions which are that the values can just be int64 or float64 and the specified column should be in the pandas.DataFrame.

import trendet
import investpy

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

test = investpy.get_stock_historical_data(stock='REP',
                                          country='Spain',
                                          from_date='01/01/2018',
                                          to_date='01/01/2019')

res = trendet.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()

Which outputs the following plot: