timeseriesql-matplotlib 0.0.1

A plotting backend for the TimeSeriesQL library

TimeSeriesQL-Matplotlib

A plotting backend for the TimeSeriesQL library

Table of Contents

• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

About The Project

This project adds a matplotlib plotting backend for the TimeSeriesQL project.

Built With

• TimeSeriesQL
• Matplotlib

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

The requirements are in the requirements.txt file.

Installation

pip

pip install timeseriesql-matplotlib

manual

1. Clone the timeseriesql-matplotlib

git clone https:://github.com/mbeale/timeseriesql-matplotlib.git

2. Install library

cd timeseriesql-matplotlib
python setup.py install 

Usage

The charting library operates on TimeSeries objects. The Axes can be overriden to control the placement of the charts. All the below examples use the following code:

import matplotlib.pyplot as plt

from timeseriesql_matplotlib import MatplotlibTQL as mp
from timeseriesql.backends.csv_backend import CSVBackend

data = CSVBackend(x for x in "AAPL.csv")[:] #CSV of AAPL stock data header = (open, close, high, low, adj close)

Line Plot

mp().line_plot(data)
plt.show()

Stacked Plot

mp().stacked_plot(data)
plt.show()

Timebox Plot

mp().line_plot(data)
timebox_plot(data[:,0])
"""
the plot arguement defaults to auto but you can set a specific period
s    - second buckets
m    - minute buckets
h    - hour buckets
d    - day buckets
mth  - month buckets
y    - year buckets
"""

Distribution Plot

mp().dist_plot(data[:,0], percentiles=[25,75]) #percentiles are optional
plt.show()

Correlogram Plot

mp().correlogram_plot(data)
plt.show()

Text Plot

mp().line_plot(data)
mp().text_plot(data[-1,0], title="A Nice Text Box", thresholds=[(0, 'green', 'white'), (20, 'cornflowerblue', 'white'), (None, 'darkorange', 'white')])

Layout Example

from matplotlib.gridspec import GridSpec

fig = plt.figure(constrained_layout=True, figsize=(20,20))

gs = GridSpec(4, 4, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[0, 2])
ax4 = fig.add_subplot(gs[0, 3])
ax5 = fig.add_subplot(gs[1:3, :3])
ax6 = fig.add_subplot(gs[1, 3])
ax7 = fig.add_subplot(gs[2, 3])
ax8 = fig.add_subplot(gs[3, :2])
ax9 = fig.add_subplot(gs[3, 2:])


mp().text_plot(data[:,0].mean(), ax=ax1, title="Avg Close")
mp().text_plot(data[:,1].mean(), ax=ax2, title="Avg High")
mp().text_plot(data[:,2].mean(), ax=ax3, title="Avg Low")
mp().line_plot(data[:,0], ax = ax4)
mp().line_plot(data, ax=ax5)
mp().line_plot(data[:,1], ax=ax6)
mp().line_plot(data[:,2], ax=ax7)
mp().line_plot(data[:,3], ax=ax8)
mp().line_plot(data[:,4], ax=ax9)

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Michael Beale - [e-mail]

Project Link: https://github.com/mbeale/timeseriesql-matplotlib