Python for Algorithmic Trading (video course) [The Python Quants]

Python for Algorithmic Trading (video training class)

This class covers topics of importance for automated, algorithmic trading with Python — build your own hedge fund with Python or become a star trader within a big institution one day

This training is about tools and techniques for algorithmic, automated trading and starts by introducing Python, NumPy & pandas

После проведения автором онлайн тренинга РІ феврале-марте 2017 РіРѕРґР° автор продает его запись (recorded training sessions) СЃРѕ СЃРєРёРґРєРѕР№ Рё СЃ учетом уже ранее купленных РєСѓСЂСЃРѕРІ ( Finance with Python Рё Python for Algorithmic Trading ). Для тех, кто уже брал участие РІ первых РґРІСѓС… его курсах Finance with Python Рё Python for Algorithmic Trading , РѕСЃРѕР±РѕР№ ценностью Р±СѓРґСѓС‚ видеозаписи данного онлайн тренинга — более 30 часов (11 записей РїРѕ 3 часа каждый).

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.
We are proud to be named Top 10 Banking Analytics Solution Provider of 2017 by Banking CIO Outlook.
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

This algorithmic trading class covers the following topics:

• Python, NumPy & pandas
• setting up the Python infrastructure
• different types of trading strategies
• vectorized backtesting of trading strategies
• event-based backtesting of trading strategies
• streaming data & socket programming
• Oanda trading platform
• Interactive Brokers trading platform
• Gemini trading platform
• automated, algorithmic trading programs
• other specialized topics

1. Intro to Python for Algorithmic Trading
This module is a general introduction to topics relevant in Python for Algorithmic Trading. It covers among others:
— open source, data, APIs, infrastructure & communities
— information and knowledge everywhere
— historical data with Eikon
— streaming data and visualization
— Quant Platform — interactive financial analytics in the browser
— online broker platforms
— algorithmic trading with less than 100 lines of code
— dataism & algorithms

2. Python Programming from Scratch
This module introduces to basic Python programming techniques, making use of Jupyter Notebook.
This module gives an overview of Jupyter Notebook usage and fundamental concepts in Python programming.
— Jupyter Notebook, media and magic commands
— testing prime characteristic of an integer
— Performance topics (algorithms, dynamic compiling)
— modelling data by Python data types and structures
— Python control structures (for loop, if-elif-else, etc.)
— Python idioms (eg list, dict, set comprehensions)
— selected Python best practices (e.g. PEP 8)

3. Numerical Computing with NumPy
This module introduces to NumPy, an important and popular Python package for numerical computations. Among others, it covers:
— arrays with Python and the array class
— regular arrays
— data selection
— reshaping & resizing
— vectorized operations
— memory layout
— structured arrays
— linear algebra
— OLS regression
— Monte Carlo simulation
— performance topics

4. Data Analysis with pandas
This module is about pandas, a powerful data analysis package. It covers, among others:
— DataFrame and Series classes
— time series handling (end-of-day, HF data)
— vectorized operations
— plotting with pandas
— grouping
— appending, merging, joining
— input-output operations

5. Setting up the Python Infrastructure
This module is about setting up an appropriate Python infrastructure. Topics covered are:
— conda for package and environment management
— Docker containers
— cloud instance with DigitalOcean

6. Working with Financial Data
This module is about working with financial data and APIs. With pandas as the main tool of choice, topics covered are:
— reading data from different sources
— writing wrappers around web-based APIs
— pro data from TR Eikon terminal/API
— storing financial data efficiently

7. Mastering Vectorized Backtesting
This module applies vectorization with pandas to backtest algorithmic trading strategies. The module covers, among others:
— vectorization
— simple moving average (SMA) based strategies
— momentum-based strategies
— mean reversion-based strategies
— classes for a systematic backtesting

8. Predicting Market Movements
Machine and deep learning are used in this module to predict market movements. It covers, among others:
— ordinary least-squares regression
— scikit-learn API
— logistic regression
— tensorflow for deep learning

9. Event-based Backtesting
Event-based backtesting allows a more realistic modelling and testing of trading strategies. The module covers, among others:
— object oriented paradigms
— backtesting base class
— long only strategies
— long short strategies
— proportional and fixed transactions costs

10. Working with Real-Time Data
Socket programming allows the easy digesting of real-time data. This module covers, among others:
— tick data server with ZeroMQ
— tick data client with ZeroMQ
— streaming plots with plotly
— real-time trend plot with plotly
— online trading algorithm (momentum)

11. CFD Trading with Oanda
The module mainly covers the Oanda API, historical data, streaming data and order placement.
— fxTrade Practice application
— Oanda API
— tradable instruments
— historical data and backtesting
— streaming data
— order placement
— automated trading

12. Stock Trading with Interactive Brokers
This module covers tpqib, a Python wrapper class for Interactive Brokers. Among others, it discusses:
— tpqib Python class
— connection objects
— contracts
— orders
— historical data
— streaming data
— real-time trading
— account data

13. Cryptocurrency Trading with Gemini
Gemini.com allows the algorithmic trading of cryptocurrencies in a modern and efficient way. Topics covered are:
— pygem Python class
— historical data
— real-time trading
— streaming data
— account information

14. Automating Strategy Execution
This module is about capital and risk management as well as deployment. It covers, among others:
— Kelly criterion
— risk management
— deployment & execution
— real-time monitoring
Some files in the attached package are new compared to the course material (from Python for Algorithmic Trading course) (eg Oanda v20 versions).

http://training.tpq.io
берем здесь https:///threads/python-for-algorithmic-trading-video-course-the-python-quants.152548/
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