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  • ARCH
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  1. Disciplines

๐Ÿ’น Economics and Finance

Previous๐Ÿ“Š Data Science and StatisticsNext๐Ÿ“š Education

Last updated 1 year ago

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ARCH

  • Description: Autoregressive Conditional Heteroskedasticity (ARCH) modeling in Python.

  • Use Case: Used for modeling and estimating volatility in financial markets.

  • Documentation:

  • GitHub Repository:

Bokeh

  • Description: A library for creating interactive visualizations for modern web browsers.

  • Use Case: Creating interactive, dynamic financial charts and visualizations.

  • Documentation:

  • GitHub Repository:

Dash by Plotly

  • Description: A Python framework for building analytical web applications.

  • Use Case: Developing interactive, web-based financial dashboards and applications.

  • Documentation:

  • GitHub Repository:

fbprophet

  • Description: A tool for producing high-quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

  • Use Case: Time series forecasting in finance, like stock price predictions or economic trend analysis.

Matplotlib

  • Description: A library for creating static, animated, and interactive visualizations in Python.

  • Use Case: Standard tool for plotting and visualizing financial data and economic models.

NumPy

  • Description: The fundamental package for numerical computation in Python.

  • Use Case: Basic numerical operations and calculations in finance and economics.

Pandas

  • Description: A powerful data analysis and manipulation library.

  • Use Case: Data manipulation and analysis, especially useful for financial time-series data.

Pandas-datareader

  • Description: A remote data access for Pandas.

  • Use Case: Extracting financial and economic data from various sources directly into Pandas DataFrames.

Plotly

  • Description: An interactive graphing library.

  • Use Case: Creating interactive financial plots and dashboards.

PyMC3

  • Description: Bayesian modeling and probabilistic machine learning with Theano.

  • Use Case: Applying Bayesian inference for econometric models and financial data analysis.

QuantLib

  • Description: A library for quantitative finance.

  • Use Case: Modeling, trading, and risk management in finance.

Quandl

  • Description: A platform for financial, economic, and alternative data.

  • Use Case: Accessing a wide range of financial and economic datasets.

Scikit-learn

  • Description: Machine learning in Python.

  • Use Case: Predictive modeling and statistical analysis in economics and finance.

Statsmodels

  • Description: Statistical modeling and econometrics in Python.

  • Use Case: Econometric and statistical analysis of financial data.

TA-Lib

  • Description: A Python wrapper for technical analysis library.

  • Use Case: Performing technical analysis on financial market data.

TensorFlow

  • Description: An end-to-end open-source platform for machine learning.

  • Use Case: Developing complex models for financial forecasting and analysis.

XGBoost

  • Description: An optimized distributed gradient boosting library.

  • Use Case: Efficient and scalable machine learning models for economic and financial data analysis.

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ARCH Documentation
ARCH GitHub
Bokeh Documentation
Bokeh GitHub
Dash Documentation
Dash GitHub
fbprophet Documentation
fbprophet GitHub
Matplotlib Documentation
Matplotlib GitHub
NumPy Documentation
NumPy GitHub
Pandas Documentation
Pandas GitHub
Pandas-datareader Documentation
Pandas-datareader GitHub
Plotly Documentation
Plotly GitHub
PyMC3 Documentation
PyMC3 GitHub
QuantLib Documentation
QuantLib GitHub
Quandl Documentation
Quandl GitHub
Scikit-learn Documentation
Scikit-learn GitHub
Statsmodels Documentation
Statsmodels GitHub
TA-Lib Documentation
TA-Lib GitHub
TensorFlow Documentation
TensorFlow GitHub
XGBoost Documentation
XGBoost GitHub
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