MiniGeek's repositories
UBS_Sample
Interactive Exploratory Data Analysis
NYCHA_Sample
2020-07_NYCHA_Sample_Repo
python-training
Python training for business analysts and traders
luisfroch.github.io
My Website
Explore_iPy_Samples
Visualizing_Financials
pandas-profiling
Create HTML profiling reports from pandas DataFrame objects
data
A collection of public data sets
bokeh-notebooks
Interactive Web Plotting with Bokeh in IPython notebook
QuantEcon.py
A community based Python library for quantitative economics
c9-python-getting-started
Sample code for Channel 9 Python for Beginners course
website
Website for QuantEcon Organisation
Concerns
Py
CoursPython
Articles et cours sur le langage Python
panel
A high-level app and dashboarding solution for Python
ipyleaflet
A Jupyter - Leaflet.js bridge
voila
Interactive renderer for Jupyter notebooks
documentation
Plotly's Documentation
ipywidgets
Interactive Widgets for the Jupyter Notebook
jupyterlab-sql
SQL GUI for JupyterLab
ipyvuetify
Jupyter widgets based on vuetify UI components
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
xleaflet
C++ backend for the jupyter leaflet widget
tidyquant
Bringing financial analysis to the tidyverse
pyfolio
Portfolio and risk analytics in Python
cufflinks
Productivity Tools for Plotly + Pandas
cookbook-2nd
IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018