Курс основан на библиотеке TradingWithPython, которую свободно можно скачать на GitHub, однако для этой библиотеки не существует инструкции в свободном доступе, что хотелось бы исправить.
Курс на английском.
The course focuses as much as possible on hands-on examples of real problems involved in quantitative trading. We will start with setting up developing environment and getting historic price data. After that we will backtest a couple of typical trading strategies. A final part of the course focuses on automated trading through Interactive Brokers API. Theoretical part (math & computer science) will be kept to a minimum and only treated where needed.
Contents
Part 1: Basics
Before taking the course you will set up your own Python environment and get a basic feel of the language. This part of the course is freely available. We will jump right in and use two case studies to get accustomed to working with scientific tooling.
Examples:
- Why Python
- Setting up Python environment
- Python basics
- Writing, running and debugging code.
- Introduction to Numpy
- Plotting with matplotlib
Part 2 : Handling the data
- Monte-carlo simulation of leveraged etfs.
Before we start with the fun part of strategy development we need to collect and sort price data. This week is about getting the data from various sources. To spice it up with a test case, we will download the entire S&P500 universe daily prices from yahoo finance.
Examples:
- Introduction to Pandas
- Working with times and dates.
- Reading and writing CSV files
- Reading excel files
- Reading HDF5 files
- Getting data from the web (Yahoo finance, CBOE, etc)
Part 3 : Backtesting strategies
- Seasonality of SPY: is there an edge based on day of the week?
- Get entire S&P500 universe history and save it to a database.
This is the fun part, which is only limited by your own creativity. We will go through several strategy test cases.
Examples:
Part 4: Going live!
- Calculating pnl and performance metrics : sharpe en drawdown
- Simple momentum strategy using moving averages
- Permanent portfolio strategy
- XLP strategy
- Pairs trading strategy (building a neutral spread and backtesting it)
- Volatility strategies
- Leveraged ETFs strategy
The final thing you need for building an automated trading system is a connection to a broker. This week we will focus on using Interactive Brokers API for receiving real-time data and submitting orders.
Examples:
- Connecting to Interactive Brokers with ibpy
- Downloading historic intraday data
- Getting real time stock data
- Placing orders
FAQ
Q: Is this course up to date?
A: The latest major update was done in 2016, when the course material was extended and updated to Python 3 version. Currently I'm working on a completely reworked version that will be released somewhere in 2017. Of course current subscribers will get access to the new version.
Q. How much does this course cost?
A. Course fee is $95 or €90
Q. Is this course content heavy? (i.e. assignments' deadlines, coding, reading materials, etc)
A. There are no deadlines, you can do the course on your own pace. Four weeks should be enough to complete first three parts of the course based on 16 hr/week study. The last part is more complex and will require different amounts of time depending on your programming experience. There will be video and/or reading material with example code. Take your time to understand the code and use the concepts for your own tasks.
Q. Will you be going through some trading strategies?
A. Yes, several typical trading strategies will be used as examples. We will take a look at a moving averages (momentum), mean reversion and pairs trading.
Q: What is included?
A: Below is a full list of included notebooks
twp_01_IPython_Notebook
twp_02_Leveraged_etfs
twp_03_Working_with_modules
twp_203_Building_a_stock_database
twp_204_Stock_screener
twp_201_Day_seasonality
twp_202_Reading_csv_files
twp_302c_backtesting-moving averages
twp_306_Volatility trading
twp_302b_backtesting
twp_304_xlp_strategy
twp_308_trading_VXX_with_nearest_neighbors
twp_303_permanent_portfolio
twp_305_spread_trading_strategy
twp_301 Performance metrics
twp_307_leveraged_etfs_ytd
twp_302_moving_averages_strat
Next to that, there are several examples on integrating with Interactive Brokers API
Q. Will the presented strategies be profitable in real trading?
A. Most probably not. Many will not be even profitable in backtest. The goal of the course is to learn how to develop your own strategies.
Q. Will there be some form of (after course) support?
A. Currently the course is offered without support, but arranging extra support is possible. Please contact me for details.
Q. Could you write an example for my trading strategy or something not included in the course material?
A. Only if you subscribed to a supported version of the course.
Q.I have coded this strategy and it runs without errors. Still I'm unsure that all calculations are correct. Could you verify my code for correctness?
A. Unfortunately this is a very difficult task, involving understanding and repeating all calculations step-by-step, which is essentially the same as rewriting the code from scratch. This is beyond the scope of course related support.
Q. Can I download and keep the course materials? (i.e. notes, video lectures, codes, etc.)
A. Yes you can keep everything. You will also have lifetime access to the course material on the site. A kind request to keep the premium content for your own use.
Q. Is there anything you can suggest that I do in order to maximize the learning benefits?
A. I believe that learning by doing is the most effective way. Decide on what your own applications are going to be (building a database, backtesting an idea, getting data from the web etc.) and reserve enough time for experimenting.
![[IMG]](/upload/parser/2023/04/226517.0.jpeg)
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