Finance Analysis

Timetk is designed to work with any time series domain. Arguably the most important is Finance. This tutorial showcases how you can perform Financial Investment and Stock Analysis at scale with pytimetk. This applied tutorial covers financial analysis with:

tk.plot_timeseries(): Visualizing financial data
tk.augment_rolling(): Moving averages
tk.augment_rolling_apply(): Rolling correlations and rolling regressions

Load the following packages before proceeding with this tutorial.

Code

import pytimetk as tk
import pandas as pd
import numpy as np

1 3 Core Properties: Financial Data

Financial data from sources like openbb or yfinance come in OHLCV format and typically include an “adjusted” price (adjusted for stock splits). This data has the 3 core properties of time series:

Timestamp: daily, hourly frequencies
Value: A price (or returns)
Groups: Stock symbols

Let’s take a look with the tk.glimpse() function.

Code

stocks_df = tk.load_dataset("stocks_daily", parse_dates = ['date'])

stocks_df.glimpse()

<class 'pandas.core.frame.DataFrame'>: 16194 rows of 8 columns
symbol:    object            ['META', 'META', 'META', 'META', 'META', 'M ...
date:      datetime64[ns]    [Timestamp('2013-01-02 00:00:00'), Timestam ...
open:      float64           [27.440000534057617, 27.8799991607666, 28.0 ...
high:      float64           [28.18000030517578, 28.46999931335449, 28.9 ...
low:       float64           [27.420000076293945, 27.59000015258789, 27. ...
close:     float64           [28.0, 27.770000457763672, 28.7600002288818 ...
volume:    int64             [69846400, 63140600, 72715400, 83781800, 45 ...
adjusted:  float64           [28.0, 27.770000457763672, 28.7600002288818 ...

2 Visualizing Financial Data

Visualizing financial data is critical for:

Quick Insights
Enhanced Decision Making
Performance Monitoring
Ease of Reporting

We can visualize financial data over time with tk.plot_timeseries():

An interactive plotly plot is returned by default. A static plot can be returned by setting engine = "plotnine".
A blue smoother is added by default. The smoother can be removed with smooth = False.

Getting More Info: tk.plot_timeseries()

Click here to see our Data Visualization Guide
Use help(tk.plot_timeseries) to review additional helpful documentation.

Plotly
Plotnine

An interactive plotly plot is returned by default. Interactive is useful for fast data exploration and for use in web apps (e.g. streamlit, shiny, dash), Click to expand code template.

Code

# plotly engine
stocks_df \
    .groupby('symbol') \
    .plot_timeseries(
        'date', 'adjusted',
        facet_ncol = 2,
        smooth = True,
        smooth_frac = 0.10,
        width = 900,
        height = 700,
        engine = 'plotly',
    )