Онлайн-курс Python for Algorithmic Trading (The Python Quants)
Об авторе Dr. Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space.
He founded The Python Quants GmbH - an independent, privately-owned analytics software provider and financial engineering boutique. The company provides Python-based financial and derivatives analytics software as well as consulting, development and training services related to Python, Open Source and Finance.
He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.
Автор данного курса также является автором 3 книг по данной теме (Python Books about Quantitative and Computational Finance):
Table of Contents
Copyright
Preface
1. Python and Algorithmic Trading
1.1. Introduction
1.2. Python for Finance
1.3. Algorithmic Trading
1.4. Python for Algorithmic Trading
1.5. Focus and Prerequisites
1.6. Trading Strategies
1.7. Overview
1.8. Conclusions
1.9. Further Resources
2. Setting up the Python Environment
2.1. Introduction
2.2. Conda as a Package Manager
2.3. Conda as a Virtual Environment Manager
2.4. Using Docker Containerization
2.5. Using Cloud Instances
2.6. Conclusions
2.7. Further Resources
3. Working with Financial Data
3.1. Introduction
3.2. Reading Financial Data From Different Sources
3.3. Working with Open Data Sources
3.4. Thomson Reuters Eikon Python API
3.5. Storing Financial Data Efficiently
3.6. Conclusions
3.7. Further Resources
3.8. Python Scripts
4. Mastering Vectorized Backtesting
4.1. Introduction
4.2. Making Use of Vectorization
4.3. Strategies based on Simple Moving Averages
4.4. Strategies based on Momentum
4.5. Strategies based on Mean-Reversion
4.6. Conclusions
4.7. Further Resources
4.8. Python Scripts
5. Predicting Market Movements with Machine Learning
5.1. Introduction
5.2. Using Linear Regression for Market Movement Prediction
5.3. Using Machine Learning for Market Movement Prediction
5.4. Using Deep Learning for Market Movement Prediction
5.5. Conclusions
5.6. Further Resources
5.7. Python Scripts
6. Building Classes for Event-based Backtesting
6.1. Introduction
6.2. Backtesting Base Class
6.3. Long Only Backtesting Class
6.4. Long Short Backtesting Class
6.5. Conclusions
6.6. Further Resources
6.7. Python Scripts
7. Working with Real-Time Data and Sockets
7.1. Introduction
7.2. Running a Simple Tick Data Server
7.3. Connecting a Simple Tick Data Client
7.4. Signal Generation in Real-Time
7.5. Visualizing Streaming Data with Plotly
7.6. Conclusions
7.7. Further Resources
7.8. Python Scripts
8. CFD Trading with Oanda
8.1. Introduction
8.2. Setting Up an Account
8.3. The Oanda API
8.4. Retrieving Historical Data
8.5. Working with Streaming Data
8.6. Implementing Trading Strategies in Real-Time
8.7. Retrieving Account Information
8.8. Conclusions
8.9. Further Resources
8.10. Python Scripts
9. Stock Trading with Interactive Brokers
9.1. Introduction
9.2. Setting up an Account
9.3. Python and the IB API
9.4. A Wrapper Class for the IB API
9.5. Retrieving Historical Data from IB
9.6. Working with Streaming Data from the IB API
9.7. Implementing Trading Strategies in Real-Time
9.8. Retrieving Account Information
9.9. Conclusions
9.10. Further Resources
9.11. Python Scripts
10. Trading Cryptocurrencies with Gemini
10.1. Introduction
10.2. Setting Up an Account
10.3. A Wrapper Class for the Gemini API
10.4. Retrieving Historical Data
10.5. Placing and Managing Orders via the API
10.6. Most Recent Transaction History
10.7. Implementing Trading Strategies in Real-Time
10.8. Retrieving Account Information
10.9. Conclusions
10.10. Further Resources
10.11. Python Scripts
11. Automating Trading Operations
11.1. Introduction
11.2. Capital Management Strategies
11.3. Risk Management
11.4. Getting the Infrastructure Ready
11.5. Deploying the Code
11.6. Live Testing
11.7. Real-Time Monitoring
11.8. Conclusions
11.9. Further Resources
11.10. Python Scripts
Appendix A: Python, NumPy, matplotlib, pandas
Introduction
Python Basics
NumPy
matplotlib
pandas
Case Study
Conclusions
Further Resources
Author Biography
Сранение данного курса «Python for Algorithmic Trading (In-Depth Online Course)» и книги «Python and Finance»:
Курс «Python for Algorithmic Trading (In-Depth Online Course)» полностью ориентирован и заточен под алгоритмическую торговлю, а книга «Python and Finance» рассказывает глобально об использовании пайтона в финансах.
В 95% случаев – в данном курсе уникальный материал, которого нет в книге.
Сравнительный анализ по темам в оглавлениях курса и книги находится в файле:
Скрытое содержимое.
Скрытое содержимое.
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Python for Algorithmic Trading
An In-Depth Online Training Course
This is an in-depth online training course about Python for Algorithmic Trading that puts you in the position to automatically trade CFDs (on currencies, indices or commodities), stocks, options and cryptocurrencies. Currently, the course material is 460+ pages in PDF form and comprises about 3,000 lines of Python code.An In-Depth Online Training Course
Об авторе Dr. Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space.
He founded The Python Quants GmbH - an independent, privately-owned analytics software provider and financial engineering boutique. The company provides Python-based financial and derivatives analytics software as well as consulting, development and training services related to Python, Open Source and Finance.
He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.
Автор данного курса также является автором 3 книг по данной теме (Python Books about Quantitative and Computational Finance):
- Python for Finance. Analyze Big Financial Data
- Derivatives Analytics with Python. Data Analysis, Models, Simulation, Calibration and Hedging
- Listed Volatility and Variance Derivatives. A Python-based Guide
Table of Contents
Copyright
Preface
1. Python and Algorithmic Trading
1.1. Introduction
1.2. Python for Finance
1.3. Algorithmic Trading
1.4. Python for Algorithmic Trading
1.5. Focus and Prerequisites
1.6. Trading Strategies
1.7. Overview
1.8. Conclusions
1.9. Further Resources
2. Setting up the Python Environment
2.1. Introduction
2.2. Conda as a Package Manager
2.3. Conda as a Virtual Environment Manager
2.4. Using Docker Containerization
2.5. Using Cloud Instances
2.6. Conclusions
2.7. Further Resources
3. Working with Financial Data
3.1. Introduction
3.2. Reading Financial Data From Different Sources
3.3. Working with Open Data Sources
3.4. Thomson Reuters Eikon Python API
3.5. Storing Financial Data Efficiently
3.6. Conclusions
3.7. Further Resources
3.8. Python Scripts
4. Mastering Vectorized Backtesting
4.1. Introduction
4.2. Making Use of Vectorization
4.3. Strategies based on Simple Moving Averages
4.4. Strategies based on Momentum
4.5. Strategies based on Mean-Reversion
4.6. Conclusions
4.7. Further Resources
4.8. Python Scripts
5. Predicting Market Movements with Machine Learning
5.1. Introduction
5.2. Using Linear Regression for Market Movement Prediction
5.3. Using Machine Learning for Market Movement Prediction
5.4. Using Deep Learning for Market Movement Prediction
5.5. Conclusions
5.6. Further Resources
5.7. Python Scripts
6. Building Classes for Event-based Backtesting
6.1. Introduction
6.2. Backtesting Base Class
6.3. Long Only Backtesting Class
6.4. Long Short Backtesting Class
6.5. Conclusions
6.6. Further Resources
6.7. Python Scripts
7. Working with Real-Time Data and Sockets
7.1. Introduction
7.2. Running a Simple Tick Data Server
7.3. Connecting a Simple Tick Data Client
7.4. Signal Generation in Real-Time
7.5. Visualizing Streaming Data with Plotly
7.6. Conclusions
7.7. Further Resources
7.8. Python Scripts
8. CFD Trading with Oanda
8.1. Introduction
8.2. Setting Up an Account
8.3. The Oanda API
8.4. Retrieving Historical Data
8.5. Working with Streaming Data
8.6. Implementing Trading Strategies in Real-Time
8.7. Retrieving Account Information
8.8. Conclusions
8.9. Further Resources
8.10. Python Scripts
9. Stock Trading with Interactive Brokers
9.1. Introduction
9.2. Setting up an Account
9.3. Python and the IB API
9.4. A Wrapper Class for the IB API
9.5. Retrieving Historical Data from IB
9.6. Working with Streaming Data from the IB API
9.7. Implementing Trading Strategies in Real-Time
9.8. Retrieving Account Information
9.9. Conclusions
9.10. Further Resources
9.11. Python Scripts
10. Trading Cryptocurrencies with Gemini
10.1. Introduction
10.2. Setting Up an Account
10.3. A Wrapper Class for the Gemini API
10.4. Retrieving Historical Data
10.5. Placing and Managing Orders via the API
10.6. Most Recent Transaction History
10.7. Implementing Trading Strategies in Real-Time
10.8. Retrieving Account Information
10.9. Conclusions
10.10. Further Resources
10.11. Python Scripts
11. Automating Trading Operations
11.1. Introduction
11.2. Capital Management Strategies
11.3. Risk Management
11.4. Getting the Infrastructure Ready
11.5. Deploying the Code
11.6. Live Testing
11.7. Real-Time Monitoring
11.8. Conclusions
11.9. Further Resources
11.10. Python Scripts
Appendix A: Python, NumPy, matplotlib, pandas
Introduction
Python Basics
NumPy
matplotlib
pandas
Case Study
Conclusions
Further Resources
Author Biography
Сранение данного курса «Python for Algorithmic Trading (In-Depth Online Course)» и книги «Python and Finance»:
Если провести сравнительный анализ по темам в оглавлении курса и книги, то можно сделать однозначный вывод:
Курс «Python for Algorithmic Trading (In-Depth Online Course)» полностью ориентирован и заточен под алгоритмическую торговлю, а книга «Python and Finance» рассказывает глобально об использовании пайтона в финансах.
В 95% случаев – в данном курсе уникальный материал, которого нет в книге.
Сравнительный анализ по темам в оглавлениях курса и книги находится в файле:
Скрытое содержимое.
Скрытое содержимое.
В качестве бесплатного бонуса к курсу предлагается запись вебкаста автора "Derivatives Analytics with Python" (In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.)
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