GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → yhilpisch → Py4fi

yhilpisch / Py4fi

Python for Finance (O'Reilly)

Labels

jupyter-notebook

Projects that are alternatives of or similar to Py4fi

Samplevae
Multi-purpose tool for sound design and music production implemented in TensorFlow.
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Novel Deep Learning Model For Traffic Sign Detection Using Capsule Networks
capsule networks that achieves outstanding performance on the German traffic sign dataset
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Basketball analytics
Repository which contains various scripts and work with various basketball statistics
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Fcos tensorflow
FCOS: Fully Convolutional One-Stage Object Detection.
Stars: ✭ 87 (-93.25%)
Mutual labels:  jupyter-notebook
Magnet
MAGNet: Multi-agents control using Graph Neural Networks
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Fairness In Ml
This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Deprecated Boot Camps
DEPRECATED: please see individual lesson repositories for current material.
Stars: ✭ 87 (-93.25%)
Mutual labels:  jupyter-notebook
Ipython Notebooks
This repository contains IPython notebooks that I have written.
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Ageprogression
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Wine Deep Learning
Exploring applications of deep learning to the world of wine
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Stanford Project Predicting Stock Prices Using A Lstm Network
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Intro2musictech
公众号“无痛入门音乐科技”开源代码
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Machine learning code
机器学习与深度学习算法示例
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Spark Nlp Models
Models and Pipelines for the Spark NLP library
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Aind Vui Capstone
AIND Term 2 -- VUI Capstone Project
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Repo2docker Action
GitHub Action for repo2docker
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Tensorfieldnetworks
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Berkeley
The Hacker Within at the University of California - Berkeley
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Deep learning
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
Dash Wind Streaming
https://plot.ly/dash/gallery/live-wind-data/
Stars: ✭ 88 (-93.17%)
Mutual labels:  jupyter-notebook
View All Similar Projects ➔

Python for Finance (O'Reilly)

This repository provides all Python codes and Jupyter Notebooks of the book Python for Finance -- Analyze Big Financial Data by Yves Hilpisch.

Oder the book here http://shop.oreilly.com/product/0636920032441.do or under http://www.amazon.com/Python-Finance-Analyze-Financial-Data/dp/1491945281/.

There are two code versions available: for Python 3.6 and Python 2.7 (in the legacy folder; not maintained anymore).

Python Packages

There is now a yaml file for the installation of required Python packages in the repository. This is to be used with the conda package manager (see https://conda.io/docs/user-guide/tasks/manage-environments.html). If you do not have Miniconda or Anaconda installed, we recommend to install Miniconda 3.6 first (see https://conda.io/miniconda.html).

After you have cloned the repository, do on the Linux/Mac shell:

cd py4fi
conda env create -f py4fi_conda.yml
source activate py4fi
cd jupyter36
jupyter notebook

On Windows, do:

cd py4fi
conda env create -f py4fi_conda.yml
activate py4fi
cd jupyter36
jupyter notebook

Then you can navigate to the Jupyter Notebook files and get started.

Yahoo! Finance & Google Finance Issues

Recently Yahoo! Finance stopped their original financial data API service that is used in the book in many different places (and been so by many others in the field for years) via the pandas-datareader package.

One way of fixing it in some places is to simply replace data_source='yahoo' by data_source='google' (and maybe working with an alternative symbol). However, Google Finance has also changed data availability via the their API such that this does only help partially.

Another way is to use this fix: https://github.com/ranaroussi/fix-yahoo-finance -- this is done for some code in chapter 11.

In the majority of cases where financial data is used, the pandas-datareader based code has been replaced by simple pandas code that accesses data files that are now part of the repository (CSV data files provided with data as provided either by the Thomson Reuters Eikon API or by data sources from FXCM Forex Capital Markets Ltd.). This makes sure that the code base is not subject to unforeseen API changes by third parties.

Quant Platform

You can immediately use all codes and Jupyter Notebooks by registering on the Quant Platform under http://oreilly.quant-platform.com.

Python for Algorithmic Trading Course & Certificate

Check out our Python for Algorithmic Trading Course under http://pyalgo.tpq.io.

Check out also our Python for Algorithmic Trading Certificate Program under http://certificate.tpq.io.

Company Information

© Dr. Yves J. Hilpisch | The Python Quants GmbH

The Quant Platform (http://pqp.io) and all codes/Jupyter notebooks come with no representations or warranties, to the extent permitted by applicable law.

http://tpq.io | [email protected] | http://twitter.com/dyjh

Quant Platform | http://oreilly.quant-platform.com

Derivatives Analytics with Python (Wiley Finance) | http://derivatives-analytics-with-python.com

Python for Finance (O'Reilly) | http://python-for-finance.com

Python for Algorithmic Trading Course | http://pyalgo.tpq.io

Python for Finance Online Training | http://training.tpq.io

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].