XQuantiPy - Financial Analysis

xquantipy is a python module for downloading macro economic data & stock data with the modules included for the analysis.

Installation

Install xquantipy using pip:

pip install xquantipy

Requirements

beautifulsoup4==4.12.2
matplotlib==3.8.0
numpy==1.26.1
pandas==2.1.2
pipreqs==0.4.13
plotly==5.18.0
pytest==7.4.0
python_dateutil==2.8.2
Requests==2.31.0
seaborn==0.13.0
setuptools==68.0.0
statsmodels==0.14.0
yfinance==0.2.29

Get started as stand-alone

Clone the repository using the following command

git clone https://github.com/iamsaicharan/XQuantiPy.git

Once cloned go into the project directory, install the requirements folder and run app.py

cd XQuantiPy
pip3 install -r requirements.txt
python3 app.py

This should start the web server in localhost in default port

Get started as python module

macro module

The Macro module helps to get the macro economic data:

from xquantipy.economics.macro import Macro

USA = Macro("USA")
IND = Macro("IND")

# Get GDP data for USA with default period of 10 Years
USA_GDP = USA.get_macros(filters=['GDP'])
# Get GDP Growth Rate & GNI for USA with period of 15 Years
USA_GDP_GROWTH_GNI = USA.get_macros(filters=['GDP_GROWTH','GNI'], period='15Y')

# Get GDP data for India with default period of 10 Years
IND_GDP = IND.get_macros(filters=['GDP'])
# Get GDP Growth Rate & GNI for India with period of 15 Years
IND_GDP_GROWTH = IND.get_macros(filters=['GDP_GROWTH', 'GNI'], period='15Y')

The Analysis module helps to get the macro economic analysis and visualization:

from xquantipy.economics.analysis import Analysis

USA = Macro("USA")
IND = Macro("IND")
Countries = MAnalysis([USA, IND])

# Get merged GDP data for the Countries
GDP_COMPARE_DF = Countries.get_merged_macro('GDP')
# Visualize GDP data for the Countries
Countries.visualize("GDP").show()

ticker module

The Ticker module helps to get the ticker data:

from xquantipy.stocks.ticker import Ticker

# Get AAPL object with default period of "10Y"
AAPL = Ticker('AAPL')
# Get GE object with period of "15Y"
GE = Ticker('GE', period='15Y')

# Get stock data with Date, Open, High, Low, Close, Adj Close, Volume, daily_return, cum_return
AAPL_DF = AAPL.data
# Get stock fundamental data in a dictionary format
AAPL_FUNDAMENTALS = AAPL.fundamentals

# Get Beta value of the stock
GE_BETA = GE.get_beta()
# Get Alpha value of the stock compared to default index "^GSPC"
GE_ALPHA = GE.get_alpha()

The Analysis module helps for analyzing ticker data:

from xquantipy.stocks.ticker import Ticker
from xquantipy.stocks.analysis import Analysis

AAPL = Ticker('AAPL')
GE = Ticker('GE')
AAPL_GE = Analysis([AAPL, GE])

# Get merged dataframes containing adj close values
AAPL_GE_DF = AAPL_GE.get_merged_adj_close()
# Visualize alpha vs beta values compared for the stocks
AAPL_GE.show_alpha_vs_beta().show()

Contributing

Want to help build XQuantiPy? Check out our CONTRIBUTING.md

License

[Project Name] is licensed under the MIT License. Please read the LICENSE: LICENSE.md file for more information.

iamsaicharan / XQuantiPy