Capital Market in Python

Author	Version	Demo
Gialdetti

capon is a python package for easily obtaining and analyzing real-time stock data. It provides extended datasets of stock metadata and features. In addition, it offers simple APIs for tracking your personal stock portfolios and their live status.

$ pip install capon

$ pip install git+https://github.com/gialdetti/capon.git

or

$ git clone https://github.com/gialdetti/capon.git
$ cd capon
$ python setup.py install

Get the historical stock price of AMD, and plot it.

import capon

amd = capon.stock('AMD', range='ytd')

The historical data is given as a standard pandas dataframe. This allows a fast and powerful data analysis, manipulation and visualization. For instance,

amd.plot(x='timestamp', y='adjclose')

Track your personal stock portfolio with real-time data.

a) Define my holdings

from capon import Portfolio, Lot

my_portfolio = Portfolio([
    Lot('2020-03-20', 'AMZN',   2, 1888.86),
    Lot('2020-03-20', 'TSLA',   8,  451.40),
    Lot('2020-03-23', 'GOOGL',  3, 1037.89),
    Lot('2020-03-23', 'AMC', 1041,    2.88),
    Lot('2020-03-27', 'ZM',    20,  150.29),
])

b) Sync with real-time stock data to find current status

status = my_portfolio.status()
display(status)

total_cost, total_value = status.sum()[['cost', 'value']]
print(f'Total cost: {total_cost:,.2f}; Market value: {total_value:,.2f}')
print(f'Total gain: {total_value-total_cost:+,.2f} ({total_value/total_cost-1:+,.2%})')

c) Plot it

from capon.visualization import plot_status
plot_status(status)

d) Plot historical data

import plotly.express as px

performance = my_portfolio.performance()
px.line(performance, x='timestamp', y='gain_pct', color='symbol', template='capon')

The full example in a live notebook is provided below.

The tutorials below aim to provide a clear and concise demonstration of some of the most important capabilities of capon. For instance, step-by-step guides for building and real-time monitoring of your portfolio, for fetching and analyzing stock historical data, or for using stocks metadata.

To make it a bit more interesting (hopefully), each tutorial first poses a meaningful stock-market "research question". In the context of answering these questions, the tutorials demonstrate the relevant library features.

Theme	MyBinder	Colab
Market Performance Visualization
My Stock Portfolio Performance
Stock Market Crash and Rebound Amid Coronavirus
Analyzing the Sector-level Crash and Rebound

After cloning and installing the development version, you can launch the test suite:

$ pytest