ubc-mds/stock_analyzer

DSCI 524 Group 20: Python package that analyzes stocks!

stock_analyzer

This is a Python package that provides basic time series modelling functionalities to analyze historical stock prices. Investment in the stock market requires not only knowledge about the listed companies, but also basic summary statistics and modellings of individual stock prices. Given time-series stock price data, this package provides key summary statistics, applies moving average and exponential smoothing models to the data, and visualizes in-sample moving average as well as exponential smoothing fits. A convenient use case for this package is to combine it with the pandas_datareader package, which can provide well-formated stock price data from Yahoo Finance dataset with customized date range setting.

Installation

$ pip install -i https://test.pypi.org/simple/ stock_analyzer

Features

The package contains the following five functions:

• summaryStats

This function calculates summary statistics including mean price, minimum price, maximum price, volatility and return rate based on daily historical stock prices. Users can specify lengths of time spans to calculate summary statistics on, and what kind of stock price measurement to use.

• movingAverage

This function applies the moving average model to all measurements of stock price and returns a pandas dataframe containing in-sample fitted values. Users can specify the length of moving average windows (unit: days).

• exponentialSmoothing

This function performs exponential smoothing on historical stock price time series data. Users can specify the alpha parameter (which defines the weighting, ranging between 0 and 1) for smoothing.

• visMovingAverage

This function creates a line chart showing the raw historical data and fitted data using the moving average method. Users are able to specify the dataframe used, the column of choice (such as 'Close', 'Adj Close') for moving average calculation, and the length of moving average window (unit: days).

• visExpSmoothing

This function creates a line chart showing the raw historical data and fitted data using the exponential smoothing method. Users are able to specify the dataframe used, the column of choice (such as 'Close', 'Adj Close') for exponential smoothing calculation, and the alpha parameter (which defines the weighting, ranging between 0 and 1) for smoothing.

Python Ecosystem

In the Python ecosystem, there are multiple packages with functionalities of time series modelling and analyses. In particular, pandas and statsmodels packages both provide functionalities to calculate summary statistics for time series data and basic time series modelling. In terms of time series visualization, packages including matplotlib, seaborn and altair all have good functionalities. However, users would need to use them separately to conduct the functionalities that this package does.

There are also multiple python packages dedicated to financial analyses. Examples include QuantPy, ffn and PyNance. There packages have similar funcitonalities to this pakcage.

Dependencies

• pytest = ^6.2.2
• pytest-cov = ^2.11.1
• codecov = ^2.1.11
• python-semantic-release = ^7.15.0
• flake8 = ^3.8.4
• Sphinx = ^3.5.1
• sphinxcontrib-napoleon = ^0.7
• matplotlib = ^3.3.4
• pandas-datareader = ^0.9.0
• pandas = ^1.2.3
• altair = ^4.1.0
• numpy = ^1.20.1

Usage

from stock_analyzer import stock_analyzer

import pandas_datareader as web
import numpy as np
import pandas as pd
import altair as alt
import warnings

df = web.DataReader('^GSPC', data_source='yahoo', start='2012-01-01', end='2020-12-17')
stock_analyzer.summaryStats(data = df, measurements=["Open", "Volume"])

 start_date	 end_date	    mean	    min	        max	    volatility	 return
    0	2020-12-07	2020-12-17	3683.394423	3656.080078	3713.649902	20.641892	0.005121
    1	2020-11-17	2020-12-17	3646.368641	3559.409912	3713.649902	44.058352	0.028624
    2	2020-11-17	2020-12-17	3646.368641	3559.409912	3713.649902	44.058352	0.028624

stock_analyzer.movingAverage(df,100,['movingAverage'+ name for name in df.columns])

            movingAverageHigh  movingAverageLow  movingAverageOpen  movingAverageClose  movingAverageVolume  movingAverageAdj Close
    Date
    2012-01-03        1284.619995       1258.859985        1258.859985         1277.060059         3.943710e+09             1277.060059
    2012-01-04        1284.561095       1258.952385        1259.041686         1277.062458         3.940199e+09             1277.062458
    2012-01-05        1284.545396       1259.016385        1259.226086         1277.102458         3.943921e+09             1277.102458
    2012-01-06        1284.517595       1259.161185        1259.446787         1277.109958         3.941052e+09             1277.109958
    2012-01-09        1284.491295       1259.318086        1259.636487         1277.146357         3.935331e+09             1277.146357
    ...                       ...               ...                ...                 ...                  ...                     ...
    2020-12-11        3459.242998       3416.176194        3438.266194         3438.738198         4.415716e+09             3438.738198
    2020-12-14        3463.419199       3420.407996        3442.302495         3442.856499         4.418761e+09             3442.856499
    2020-12-15        3468.099500       3425.003696        3446.780793         3447.646401         4.423414e+09             3447.646401
    2020-12-16        3472.797900       3429.746897        3451.544893         3452.264001         4.424345e+09             3452.264001
    2020-12-17        3477.611902       3434.693899        3456.338691         3457.304402         4.425915e+09 

stock_analyzer.exponentialSmoothing(df,['exponentialSmoothing'+ name for name in df.columns])

                             exponentialSmoothingHigh exponentialSmoothingLow	exponentialSmoothingOpen	exponentialSmoothingClose	exponentialSmoothingVolume	exponentialSmoothingAdj Close
    Date						
    2012-01-03	                      1284.619995	         1258.859985	                1258.859985	                 1277.060059	         3.943710e+09	                1277.060059
    2012-01-04	                      1282.852991	         1261.631982	                1264.310999	                 1277.132056	         3.838371e+09	                1277.132056
    2012-01-05	                      1282.912108	         1262.720391	                1268.207714	                 1278.310457	         3.981645e+09	                1278.310457
    2012-01-06	                      1282.590465	         1265.906263	                1272.024416	                 1278.160337	         3.884200e+09	                1278.160337
    2012-01-09	                      1282.410323	         1268.499399	                1273.766078	                 1278.922221	         3.730420e+09	                1278.922221
    ...	                                      ...	                 ...	                        ...	                         ...	                  ...	                        ...
    2020-12-11	                      3683.080578	         3649.520587	                3668.977441	                 3671.981068	         4.714086e+09	                3671.981068
    2020-12-14	                      3687.439437	         3648.416438	                3670.865215	                 3664.633745	         4.678337e+09	                3664.633745
    2020-12-15	                      3689.794617	         3651.777541	                3669.528624	                 3673.629656	         4.582920e+09	                3673.629656
    2020-12-16	                      3696.237238	         3662.815300	                3677.545037	                 3681.891736	         4.425129e+09	                3681.891736
    2020-12-17	                      3704.902102	         3677.231745	                3688.376496	                 3694.068209

stock_analyzer.visMovingAverage(df,'Close', 50)

stock_analyzer.visExpSmoothing(df,'Close', 0.3)

Documentation

The official documentation is hosted on Read the Docs: https://stock_analyzer.readthedocs.io/en/latest/

Contributors

We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.

• Kangyu (Mark) Wang
• Sicheng (Marc) Sun
• William Xu
• Tingyu Zhang

Credits

This package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from the pyOpenSci/cookiecutter-pyopensci project template and the audreyr/cookiecutter-pypackage.

stock_analyzer's Issues

Edit CONDUCT.md to include the code of conduct for contributing to your project

Team work contract

visualize functions

Friday 1pm Meeting Zoom Link

https://us04web.zoom.us/j/79921485252?pwd=ZVJLbXJwUHlkRWJhQlNoemEvVFpyUT09

passcode: Z5jTNc

Edit CONTRIBUTORS.md to add the contributor names (the students on the teams)

writing codes for summary statistics

• main body of the function
• unit tests

exponential smoothing function

writing codes for data visualizations

• main body of the function
• unit tests

writing codes for exponential smoothing

• main body of the function
• unit tests

GitHub actions workflow for continuous deployment

A couple things you might need to change in your .github/workflows/deploy.yml file:

• Ensure the deployment branch (typically the main branch) is correctly specified on lines 7
• Ensure that the correct Python version is listed on line 14 (it should match the version in your pyproject.toml file)

moving average function

Manage issues

• Project boards & Milestones
• Assign issue to each member

Documentation

We are experiencing technical difficulties with the display of the submodule. The docs badge showed pass with no issue after we created Read the Docs build. The docstrings were showing perfectly fine on all our local machines. Tiffany is helping us debug. Updates to be continued.

Check whether the code passes the unit tests you have written using the pytest package

Exception handling

• summary statistics
• moving average
• exponential smoothing
• data visualizaions

summary statistics function

License checklist

Use proper grammar and full sentences throughout the project, especially in your README

Function specification brainstorming （From William）

• Data visualization
• Log-transformation
• Moving average, some summary statistics
• Exponential smoothing
• Prediction (if time permits)

Outline the package you would like to build in the README.md

• a summary paragraph that describes the project at a high level
• a bulleted list of the functions (and datasets if applicable) that will be included in the package
• a paragraph describing where your packages fit into the Python ecosystem

Create project structure

writing codes for moving average

• main body of the function
• unit tests

Saturday Final Review Meeting at 2:30pm

Create project structure for the R project

Agree upon collaboration strategy (Github flow) and edit on CONTRIBUTING.md

Make sure that the tests cover all branches

Github actions workflow for continuous integration

A couple things you might need to change in your .github/workflows/build.yml file:

• Ensure the deployment branch (typically the main branch) is correctly specified on lines 7 & 10
• Ensure that the correct Python version is listed on lines 21 and 24 (it should match the version in your pyproject.toml file)

Docstrings for functions

• moving average (Tingyu Zhang)
• exponential smoothing (Sicheng Sun)
• summary statistics (Mark (Kangyu) Wang)
• Data visualization (William(Yihang) Xu)