Py4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

Related tags

Deep Learningpy4fi2nd
Overview

Python for Finance (2nd ed., O'Reilly)

This repository provides all Python codes and Jupyter Notebooks of the book Python for Finance -- Mastering Data-Driven Finance (2nd edition) by Yves Hilpisch.

Visit the book page of O'Reilly under http://bit.ly/python-finance-2e or order the book under https://www.amazon.com/Python-Finance-Mastering-Data-Driven/dp/1492024333/.

The codes of the book are based on Python 3.7. The codes presented in this Github repository are tested for Python 3.6 since at the time of creating it, TensorFlow was not yet compatible with Python 3.7. Once this has happened, appropriate changes (e.g. to the conda yaml file, see below) will be made.

Python Packages

There is 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 3.7 first (see https://conda.io/miniconda.html).

After you have cloned the repository, do on the shell (currently works on Mac OS):

cd py4fi2nd
conda env create -f py4fi2nd.yml
source activate py4fi2nd
jupyter notebook

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

Quant Platform

You can immediately use all codes and Jupyter Notebooks by registering on the Quant Platform under http://py4fi.pqp.io.

Python for Finance Training & University Certificate

Check out our Python for Finance & Algorithmic Trading online trainings under http://training.tpq.io.

Check out also our University Certificate Program in Python for Algorithmic Trading 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://pqp.io

Derivatives Analytics with Python (Wiley Finance) | http://dawp.tpq.io

Python for Finance (O'Reilly) | http://pff.tpq.io

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

University Certificate in Python for Algorithmic Trading | http://certificate.tpq.io

Owner
Yves Hilpisch
CEO The Python Quants & The AI Machine | Adjunct Professor of Computational Finance | Python, AI, Finance & Algorithmic Trading
Yves Hilpisch
object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

赛题背景 在商品知识产权领域,知识产权体现为在线商品的设计和品牌。不幸的是,在每一天,存在着非法商户通过一些对抗手段干扰商标识别来逃避侵权,这带来了很高的知识产权风险和财务损失。为了促进先进的多媒体人工智能技术的发展,以保护企业来之不易的创作和想法免受恶意使用和剽窃,因此提出了鲁棒性标识检测挑战赛

65 Dec 22, 2022
Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021

MarioNette | Webpage | Paper | Video MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Ale

Dima Smirnov 28 Nov 18, 2022
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022
ConvMixer unofficial implementation

ConvMixer ConvMixer 非官方实现 pytorch 版本已经实现。 nets 是重构版本 ,test 是官方代码 感兴趣小伙伴可以对照看一下。 keras 已经实现 tf2.x 中 是tensorflow 2 版本 gelu 激活函数要求 tf=2.4 否则使用入下代码代替gelu

Jian Tengfei 8 Jul 11, 2022
Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Noisy Natural Gradient as Variational Inference PyTorch implementation of Noisy Natural Gradient as Variational Inference. Requirements Python 3 Pytor

Tony JiHyun Kim 119 Dec 02, 2022
AdaFocus (ICCV 2021) Adaptive Focus for Efficient Video Recognition

AdaFocus (ICCV 2021) This repo contains the official code and pre-trained models for AdaFocus. Adaptive Focus for Efficient Video Recognition Referenc

Rainforest Wang 115 Dec 21, 2022
Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21'

Argument Extraction by Generation Code for paper "Document-Level Argument Extraction by Conditional Generation". NAACL 21' Dependencies pytorch=1.6 tr

Zoey Li 87 Dec 26, 2022
Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

advantage-weighted-regression Implementation of Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, by Peng et al. (

Omar D. Domingues 1 Dec 02, 2021
Python scripts for performing lane detection using the LSTR model in ONNX

ONNX LSTR Lane Detection Python scripts for performing lane detection using the Lane Shape Prediction with Transformers (LSTR) model in ONNX. Requirem

Ibai Gorordo 29 Aug 30, 2022
Detect roadway lanes using Python OpenCV for project during the 5th semester at DHBW Stuttgart for lecture in digital image processing.

Find Line Detection (Image Processing) Identifying lanes of the road is very common task that human driver performs. It's important to keep the vehicl

LMF 4 Jun 21, 2022
Multi-layer convolutional LSTM with Pytorch

Convolution_LSTM_pytorch Thanks for your attention. I haven't got time to maintain this repo for a long time. I recommend this repo which provides an

Zijie Zhuang 733 Dec 30, 2022
Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

Wenxuan Zhou 146 Nov 29, 2022
Official implementation of Densely connected normalizing flows

Densely connected normalizing flows This repository is the official implementation of NeurIPS 2021 paper Densely connected normalizing flows. Poster a

Matej Grcić 31 Dec 12, 2022
MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

Secomp Lab at Purdue University 34 Dec 14, 2022
Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

Multi-Anchor Active Domain Adaptation for Semantic Segmentation Munan Ning*, Donghuan Lu*, Dong Wei†, Cheng Bian, Chenglang Yuan, Shuang Yu, Kai Ma, Y

Munan Ning 36 Dec 07, 2022
Open Source Light Field Toolbox for Super-Resolution

BasicLFSR BasicLFSR is an open-source and easy-to-use Light Field (LF) image Super-Ressolution (SR) toolbox based on PyTorch, including a collection o

Squidward 50 Nov 18, 2022
Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

SML (ICCV 2021, Oral) : Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Standardi

SangHun 61 Dec 27, 2022
Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

43 Dec 26, 2022
STRIVE: Scene Text Replacement In Videos

STRIVE: Scene Text Replacement In Videos Dataset Types: RoboText SynthText RealWorld videos RoboText : Videos of texts collected using navigation robo

15 Jul 11, 2022
Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

3 Sep 19, 2022