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.
$ pip install -i https://test.pypi.org/simple/ stock_analyzerThe 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.
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.
- 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
from stock_analyzer import stock_analyzer
import pandas_datareader as web
import numpy as np
import pandas as pd
import altair as alt
import warningsdf = 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)
The official documentation is hosted on Read the Docs: https://stock_analyzer.readthedocs.io/en/latest/
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
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.