pytrend 0.3

Trend detection on stock time series data

Pytrend - Trend detection on stock time series data

Introduction

pytrend is a Python package to detect trends on the market so to analyze its behaviour. So on, this package has been created to support Yahoo Finance features when it comes to data retrieval from different financial products such as stocks, funds or ETFs; and it is intended to be combined with it, but also with every pandas.DataFrame, formatted as OHLC.

Anyways, pytrend can also be used to identify trends from any pandas.DataFrame which contains any column with int64 or float64 values, even though it is intended to be used with stock data; it can also be used for any pandas.DataFrame.

Installation

In order to get this package working you will need to install it using pip by typing on the terminal:

$ python -m pip install pytrend --upgrade

Or just install the current release or a specific release version such as:

$ python -m pip install pytrend==0.3

Or install from the source

$ git clone https://github.com/dopevog/pytrend.git
$ cd pytrend
$ python setup.py install

Usage

As pytrend 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.

In the example presented below, the identify_all_trends function will be used to detect every bearish/bullish trend with a time window above 5 days, which, for example, implies that every bearish (decreasing) trend with a longer length than 5 days will be identified as a down trend 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 pytrend

import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = pytrend.identify_all_trends(stock='AAPl',
                                 from_date='06/01/2020',
                                 to_date='04/01/2021',
                                 window_size=5,
                                 identify='both')

df.reset_index(inplace=True)

plt.figure(figsize=(20, 10))

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

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

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

    ax.axvspan(df[df['Up Trend'] == label].index[0],
               df[df['Up Trend'] == label].index[-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].index,
                 y=df[df['Down Trend'] == label]['Close'],
                 color='red')

    ax.axvspan(df[df['Down Trend'] == label].index[0],
               df[df['Down Trend'] == label].index[-1],
               alpha=0.2,
               color='red')
               
locs, _ = plt.xticks()
labels = []

for position in locs[1:-1]:
    labels.append(str(df['Date'].loc[position])[:-9])

plt.xticks(locs[1:-1], labels)
plt.show()

Further usage insights can be found on the docs. Anyways, feel free to create your own scripts on how you use pytrend or how can it be used in order to improve its features.

License

This Project Has Been MIT Licensed